Niels Henze
email: niels.henze@uni-oldenburg.de

Susanne Boll
email: susanne.boll@uni-oldenburg.de

Who's that girl? Handheld Augmented Reality for Printed Photo Books

Keywords: augmented reality, mobile phone, photo sharing, mobile interaction, image analysis, photo book

Augmented reality on mobile phones has recently made major progress. Lightweight, markerless object recognition and tracking makes handheld Augmented Reality feasible for new application domains. As this field is technology driven the interface design has mostly been neglected. In this paper we investigate visualization techniques for augmenting printed documents using handheld Augmented Reality. We selected the augmentation of printed photo books as our application domain because photo books are enduring artefacts that often have online galleries containing further information as digital counterpart. Based on an initial study, we designed two augmentations and three techniques to select regions in photos. In an experiment, we compare an augmentation that is aligned to the phone's display with an augmentation aligned to the physical object. We conclude that an object aligned presentation is more usable. For selecting regions we show that participants are more satisfied using simple touch input compared to Augmented Reality based input techniques.

Introduction

The paperless office was predicted more than a quarter century ago. Despite the availability of desktop computers, notebooks and smartphones everywhere at any time this revolution has not happened yet [START_REF] Sellen | The myth of the paperless office[END_REF]. Paper documents have qualities that current digital device fail to offer: paper has an amazing resolution, does not need a power supply, and remains accessible for centuries; furthermore, paper is cheap and one can leave it lying around without much concern about thievery or environmental influences. The digital revolution, however, happened in the last decades. While the digital world is increasingly getting dynamic and interactive -paper remains static. Once printed or written the content remains. Today, paper documents are usually produced from digital content and printing is the end of the production chain. Related digital media is often available at production time but is not used for particular prints. Digital content might also evolve, get annotated, iterated and improved but the once printed document provides no access to these changes. What is missing is a way to unify the advantages of printed documents with the flexibility and richness of online content.

Using mobile devices in conjunction with physical objects has been proposed to unify the advantages of paper documents with the interactivity, personalization, and real-time features provided by digital media [START_REF] Fitzmaurice | Situated information spaces and spatially aware palmtop computers[END_REF]. Augmented Reality (AR) looks as a natural option to fuse digital media with paper documents [START_REF] Hull | Paper-Based Augmented Reality[END_REF]. As traditional AR has severe technical limitation, such as the need for AR goggles, handheld AR recently received a great share of attention in the AR domain [START_REF] Wagner | History and Future of Tracking for Mobile Phone Augmented Reality[END_REF]. Smartphones are used to present the phone's camera image on the screen augmented with additional content that is aligned to the recorded physical world. Recent technical development has made major progress beyond the use of artificial markers towards augmenting diverse types and objects. Handheld AR research is currently dominated by technical development. New algorithms make handheld AR feasible for more and more different types of object, incremental improvement increases the accuracy, and combining different techniques greatly improved the performance [START_REF] Wagner | History and Future of Tracking for Mobile Phone Augmented Reality[END_REF]. The interface design, however, has been mostly neglected so far. The HCI community only scratched the surface of designing handheld AR interfaces.

The application domain we selected to investigate the interface design of handheld AR systems is the interaction with printed photo books. Printed photo books are highly emotional enduring artefacts, handcrafted by users to preserve memories and often have interactive online galleries as natural counterparts. To create a connection between the physical and the digital, we try to enable users accessing the same information available in photo communities' galleries using physical photo books.

In this paper, we use photo books to investigate two fundamental aspects of handheld AR for printed documents: How to visualize annotations and how to select regions to create annotations. In the following Section we discuss related research. Afterwards, we motivate the augmentation of printed photo books in Section 3 and we present an initial study to collect design approaches in Section 4. The visualization techniques and selection techniques are designed in Section 5. Section 6 describes the design and the results of the experiment that compares the designed approaches. We close the paper with conclusions and an outlook to future work.

Related Work

Several approaches have been developed to create the link between physical objects and digital content. Fitzmaurice was one of the first who predicted the use of mobile devices for interaction with physical objects by simply pointing at them. He described for instance an application with which the user can point at locations on a physical map with a mobile device to get additional information [START_REF] Fitzmaurice | Situated information spaces and spatially aware palmtop computers[END_REF]. In recent years more systems emerged that provide information related to physical objects using mobile phones. Common implementation mark objects either with visual markers [START_REF] Rohs | Using camera-equipped mobile phones for interacting with realworld objects[END_REF] or with electronic tags [START_REF] Want | Bridging physical and virtual worlds with electronic tags[END_REF]. Barcodes can be seen as the first implementation of visual markers back in 1948. Since special barcode readers are needed to read 1D barcodes 2D barcodes such as QR-codes [START_REF]International Organization for Standardization: Information Technology. Automatic Identification and Data Capture Techniques -Bar Code Symbology -QR Code[END_REF] have been developed. QR-codes can be read using recent mobile phones and a few mobile phones also have an integrated RFID reader to read electronic tags. However, not all physical objects are suitable for markers. Sights and buildings are simply too large or out of range to be reasonably equipped with either type of markers. It is also questionable if objects, whose visual appeal is important, such as printed photo books, can in general be equipped with visual markers. Not only because the markers require visual space but also because visual markers affect the design of the object. Electronic marker, however, lead to ambiguity for applications with a high object density. Furthermore all markers restrict the interaction radius in a specific way. In particular, electronic markers do not to provide the relative location of the object.

Another approach to create the link between physical objects and digital content is using content-based object recognition. Davies et al. investigated user reaction to the use of a digital image capture and recognition system for mobile phone to access information about sights [START_REF] Davies | Understanding the role of image recognition in mobile tour guides[END_REF]. With their system users can take a photo of a sight with a mobile phone and receives a related description. Davies et al. found that half of the users want to use such a system even when "this is a more complex, lengthy and error-prone process than traditional solutions". Pielot et al. compared the use of object recognition and typing an URL to access information about posters [START_REF] Pielot | Evaluation of Camera Phone Based Interaction to Access Information Related to Posters[END_REF] with a mobile phone. They showed that users are faster using object recognition and also prefer this interaction technique. In our own work, we showed that even novice users can use this interaction to get information about printed photos [START_REF] Henze | Snap and share your photobooks[END_REF].

Handheld AR (also called Magic Lens) is the adaptation of AR for mobile phones. It extends the concept of using discrete photos by providing immediate feedback using an augmentation. Prototypes that augment different types of physical objects, such as text-heavy conference proceedings [START_REF] Hull | Paper-Based Augmented Reality[END_REF], real estate guides [START_REF] Erol | HOTPAPER: multimedia interaction with paper using mobile phones[END_REF], and sights [START_REF] Alessandro | Zooming interfaces for augmented reality browsers[END_REF] have been developed. Different groups (e.g. [START_REF] Rohs | Map navigation with mobile devices: virtual versus physical movement with and without visual context[END_REF][START_REF] Morrison | Like bees around the hive: a comparative study of a mobile augmented reality map[END_REF][START_REF] Henze | Evaluation of an Off-Screen Visualization for Magic Lens and Dynamic Peephole Interfaces[END_REF]) developed prototypes to augment paper maps and conducted according user studies. Rohs et al. compared users' performance in a find-and-select task using a joystick controlled "static peephole", a dynamic peephole and a handheld AR interface (called Magic Lens) [START_REF] Rohs | Map navigation with mobile devices: virtual versus physical movement with and without visual context[END_REF]. The study showed that the dynamic peephole and Magic Lens clearly outperform joystick navigation. Morrison et al. compared handheld AR for a paper map with a digital map [START_REF] Morrison | Like bees around the hive: a comparative study of a mobile augmented reality map[END_REF] in a real world setting. One of their conclusion is that the "main potential of (handheld) AR maps lies in their use as a collaborative tool".

A number of studies showed encouraging results for handheld AR systems compared to traditional approaches and researchers investigated fundamental aspects, such as the adaptation of Fitts' law. Much less work addresses the interface design of those systems. Henze et al. proposed different interface designs for a system that augments music records based on a user study [START_REF] Henze | Designing a CD augmentation for mobile phones[END_REF]. Liao et al. implemented selection and interaction techniques for printed conference proceedings [START_REF] Liao | Pacer: Fine-grained interactive paper via cameratouch hybrid gestures on a cell phone[END_REF]. Based on an initial study they identified challenges for future work (e.g. slow image recognition and inaccurate document registration). What is missing is an understanding of the alternatives of the interface design beyond qualitative results.

Interaction with printed photo books

In our work, we investigate solutions that close the gap between printed documents and the digital realms. A particular application area that we currently focus on is the interaction with printed photo books [START_REF] Henze | Snap and share your photobooks[END_REF]. Photo books are enduring artefacts hand-crafted by users to preserve memories and often have online galleries as natural counterparts. To create a connection between the physical and the digital we try to enable users to access the information available in photo communities' galleries using their physical photo books. By combining digital content, such as comments, music, or videos, with the printed photo book, the photo book could be brought to life.

Photo books are often used by individuals or small groups possibly to tell about the last vacation, the good old times or more generally to share memories. Frohlich et al., for example, found that photos are mainly used to share memories and that "sharing photos in person was described as most common and enjoyable" [START_REF] Frohlich | Requirements for photoware[END_REF]. People use the photos to "share the memory", and Frohlich also reports that people use printed photos for "jointly 'finding' the memory". Crabtree et al. emphasize that the sharing of printed photos relies upon the distribution of photos across group views and personal views [START_REF] Crabtree | Collaborating around collections: informing the continued development of photoware[END_REF]. The following scenario sketches a typical situation that could emerge around photo books:

Mary has invited her family to celebrate her birthday. After welcoming all guests and receiving birthday gifts Mary digs out a couple of photo books she produced for recent events such as her trip to Mexico, her son's baptism, and her daughter's school enrolment. A lively discussion about the events emerges between the guests and the photo books are passed from one to the other. While Mary is chatting about Mexico with her brother, her grandparents discuss the baptism in detail and her nephews and nieces debate about the school enrolment.

While the photo books are a great means to share memories as described above, they come with a few limitations. Content has been selected to create a visual appealing and affordable photo book. More information, e.g. the names of the persons on the photos or comments, as well as additional photos and videos are often available at production time. This background information remains inaccessible using the plain photo book. By augmenting the photos each user could access additional information that are of particular interest to him or her in the specific situation. Mary's brother might want to learn where the photos have been taken exactly and get more information about the photographed sights. He might also be interested in the additional video snippets Mary took during her vacation. Digital content evolves and different users can annotate the content with additional information. Mary's grandparents are, for example, interested in who attended the baptism. Mary would be happy if her parents could provide more information about distant relatives on the photos and if they would add names to the photos printed in the book. Mary's nephews and nieces are probably less interested in the family events but want to get entertained. An audio drama prepared by Mary could connect the photos to an age-specific funny storytelling of the event turning the photo book into an audible picture book for kids.

Requirements and participatory design

In order to design the handheld AR interface for printed photo books we started with an initial study to collect features and proposals for the interface design. We collected information and features participants want to access using their printed photo book. Furthermore, we asked participants to propose designs to visualize information with handheld AR using pen and paper.

Methodology and Participants

In the beginning of the study we introduced the participants to the studies purpose and collected demographic data. The remainder of the study was split into two halves. In the first part we asked participants to write down a list of information and features they consider relevant. Participants were also asked to rate each of the named features on a five point Likert scale (from not important to very important). Afterwards, we asked the participants to draw an augmentation on printed images containing a mobile phone that shows an unaugmented image of a photo book on its screen (see Figure 1). The layout of the used photo books is consistent with the sparse knowledge gained from analysing photo books [START_REF] Sandhaus | From usage to annotation: analysis of personal photo albums for semantic photo understanding[END_REF] and is also consistent with image composition algorithms for photo books [START_REF] Atkins | Blocked recursive image composition[END_REF].

12 persons (8 male) participated in the study. The participants' age was between 8 and 54 years and the average age was 32.6 years. 4 participants had a technical background (undergraduate and graduate students) and 8 had no technical background.

Results

In the following we report the results from the user study. First the results for the desired features and information are described, followed by an outline of the sketched interfaces.

Information and features

The participants named 6.83 (std=3.43) different information/features on average. We normalized the results by merging synonyms and very similar answers. Table 1 shows the average rating for aspects that have been mentioned at least three times. The results can be further reduced considering that recording time and date is very similar information that is often presented side-by-side. Persons' names and object descriptions are also similar information that describes specific parts of a photo. Most participants proposed to not only display information but requested the possibility to also create or change additional content. In particular, participants wanted to add descriptions of persons, sights and other objects to photos similar to the way photos can be annotated in online galleries on Flickr and Facebook.

Visualization The visualizations proposed by the participants can be differentiated by the way the overlay is aligned. Six participants aligned the information to the border of the phone. Figure 1 (left) shows an example of a sketch where the information is presented at the phone's border. The information is located at either one or two sides of the display. In contrast, three participants aligned all information to the photo. Figure 1 (right) shows one of these sketches that presents the information at the photo's border. The other participants choose a mixed design where some information is aligned to the phone's border and other information is aligned to the photo itself. Particularly, information that describes only parts of a photo is aligned to this part while general information about a photo, such as its title, is located at the border of the phone's display.

Ten participants explicitly suggested highlighting the recognized photos of the photo book in some way. Seven participants proposed to draw a rectangle around the photos. Other participants suggested to gray out the background or did not specify a particular way. Even though not requested, six participants proposed to have a way to activate additional functionalities on a separate view. Two participants proposed using the phone's menu button and two proposed to use icons (e.g. a video icon) that lead to the separated view. The other two participants did not specify a particular way to activate the additional functionalities.

Discussion

The information that has been requested most often by the participant describes the photos content such as, persons and objects that have been photographed. When and where a photo has been taken is almost equally important. More general information textual description of a photo such as tags and a title ranks third. Participants could also envisage including social features such as comments or a pointer to social networks. In general, it can be differentiated between information that describe particular parts of a photo (e.g. the name of a person), information that describes the whole photo (e.g. recording time or a title), and content provided by other users (e.g. comments). Most participants do not only want to view information but also want to add information. They propose to be able to select regions of a photo to tag persons, sights, and other objects.

The sketches for the visualization of the augmentation produced by the participants revealed three different pattern that are consistent with a the results of a similar study Henze et al. conducted for augmenting physical music records [START_REF] Henze | Designing a CD augmentation for mobile phones[END_REF]. Participants align the elements either to the augmented object or they align them to the phone's border. Furthermore, some participants propose mixed designs. Most participants propose to highlight the augmented object. While not all participants proposed the highlighting of augmented objects explicitly in both studies we assume that this a general demand.

Design and implementation

Based on the results of the initial study we designed an interface to interact with printed photo book. In addition to a pure visualization of information we decided to also design interactions to select regions of photos, in order to tag persons, sights, and other objects, as this has been requested by the participants. In the following, we describe the design of the augmentations and the designs of the selection techniques. Afterwards, the implementation of the resulting prototype is outlined.

Augmentation design

The proposed designs for the augmentation can be divided in those were the placement of information is aligned to the phone and those were the information is aligned to the object. Furthermore, some participants proposed a mixture of both approaches. We decided to not design a mixed augmentation in order to investigate the alignment aspect without the ambiguity of a mixed design. The participants proposed a number of information items they consider important to visualize. The information can be distinguished in information that describes a whole photo and information that describes a certain part of a photo. In order to support both types we decided to support a title for the photos and annotation of persons and objects in a photo. The title is a representative for information that describes the photo in general while the annotation of persons and objects is the representative for information that describes certain parts.

As proposed by the participants photos and annotated regions are highlighted. For all visualization and annotation techniques the pages of the photo book are highlighted by displaying only the page with colours and leaving the surrounding greyed out. Furthermore, individual photos and annotated regions of a photo are highlighted by drawing a rectangle around them. The centre of the display is marked with a crosshair. The advantage of this design is that all information is visible at the same time. As the text is shown as an augmentation the position and the size of the text changes according to the augmented object. Thus, the size of the presented text can naturally be increased and decreased by changing the distance of the phone to the photo book. One disadvantage of this design is that the augmentation becomes small if the phone is far from the photo. Thereby, text might become difficult to read if a user wants to get an overview about the information available for a photo book page. Furthermore, the text permanently moves and wobbles if the user moves the phone. Thereby, readability of text is affected by accidental movement of the phone.

Photo-aligned augmentation

Phone-aligned augmentation

The second design, shown in Figure 2 (right), aligns the information to the phone. The photos' titles are at the top of the screen while the descriptions of particular regions are at the bottom. To connect the textual description of a region with the region in the photo a rectangle is draw around the region and a line connects the description with the region. Thus, the text is always located at the same position. Information is only displayed for the photo that is located below the crosshair that marks the centre of the screen. Thus, even if multiple photos are visible in the camera image only the information for one photo is displayed. This design has the advantage that the text has always the same readable size. As the text stays at the same position as soon as the crosshair is above a photo readability is not affected by the movement of the phone. The design's disadvantage is that the information for only one photo is visible at a time. Thereby, the user must move the crosshair across all photos on a photo book page to get an overview about the available information. Since the text does not change its position selecting it would be less affected by accidental movement of the phone.

Interaction techniques to select regions

As annotating regions has been requested in the initial study we designed three selection techniques to mark regions of a photo. As we did not collect recommendation for designing this interaction from participants we designed three fundamentally different approaches. With the first two selection techniques users select regions in the reference system of the augmentation. They either by have to move the phone or touch on the display. We included a third technique where regions are marked by touching a separate static image as a baseline. We did not include the selection techniques Liao et al. proposed with the PACER system [START_REF] Liao | Pacer: Fine-grained interactive paper via cameratouch hybrid gestures on a cell phone[END_REF] because we aim at true handheld AR instead of loose registration and we cannot exploit knowledge about distinct document regions (e.g. words and sentences). The two techniques touch-based techniques can, however, be seen as the basic concepts that are combined in PACER.

Crosshair-based region selection

With the first technique the user aims with the crosshair that is located in the centre of the display at a corner of the region that should be selected. The technique is illustrated in Figure 3. The technique is inspired by handheld AR systems that use a crosshair in the centre of the screen to select predefined objects (e.g. [START_REF] Rohs | Target acquisition with camera phones when used as magic lenses[END_REF]). By touching the display at any position the user defines the first corner (e.g. the top-left corner) of the region. The user than has to move the crosshair to the opposite corner (e.g. the bottom-right corner) by physically moving the phone while touching the display. The region is marked when the user stops touching the screen. As the region is created in the reference system of the augmentation the created rectangle is aligned to the photo. The advantage of this technique is that the "fat-finger problem" (i.e. that users using touchscreens occlude the area they want to touch) is avoided. As the location at which the crosshair aims can be estimated more precisely than the position in which a touch results it might also be more precisely. As the user can zoom (by changing the distance to the photo) while moving the crosshair, the region could be created more precisely. A disadvantage is that the device must be physically moved. This could lead to a higher physical and mental demand and makes the technique prone to accidental movement of the phone.

Augmented touch-based region selection

With the second technique, shown in Figure 4, the user touches at a corner of the region that should be marked to define the first corner. After moving the finger to the opposite corner the region is marked when the user lifts the finger from the screen. As the region is created in the reference system of the augmentation the created rectangle is aligned to the photo even if the user rotates the phone. This technique is inspired by handheld AR systems where users have to touch the augmentation of predefined objects to select them (e.g. [START_REF] Henze | Evaluation of an Off-Screen Visualization for Magic Lens and Dynamic Peephole Interfaces[END_REF]). The advantage of this interaction technique is that the user does not have to physically move the phone. Therefore, it might be less physically and mentally demanding.

The user can, however, imitate the crosshair-based approach by not moving the finger but only the phone. In this case the techniques have almost the same advantages and disadvantages as the crosshair-based approach. In any case the technique is affected by the "fat-finger problem".

Unaugmented touch-based region selection

The third technique, shown in Figure 5, serves as a baseline that works on a static image of the photo that should be annotated. As with the previous technique the user touches at a corner of the region that should be marked to define the first corner. The user than has to move the finger to the opposite corner. The region is marked when the finger is lifted from the screen. We did not use more sophisticate selection techniques because the other techniques would benefit similarly from more sophisticated interaction techniques. The clear advantage of this technique is that the user can freely move the phone as the phone is disconnected from the physical photo. Thus, unintentional jitter is avoided but using a separated view can be an imminent disadvantage for a handheld AR system. Another limitation is that the user occludes the area where she aims at with the finger. For this concrete design (but not generally) a further limitation is the lack of zoom.

Prototypical implementation

In order to implement the designed visualization and interaction techniques we needed to implement an Augmented Reality system for mobile phones. AR systems estimate the pose of the augmenting display in relation to the scene or object that should be augmented. Using this pose a system can transform the augmenting overlay into the reference system of the physical scene and render the augmentation.

As we aim to preserve the photo book's visual appeal visual markers (e.g. QRcodes) cannot be used. Text-based document recognition [START_REF] Erol | HOTPAPER: multimedia interaction with paper using mobile phones[END_REF][START_REF] Hull | Paper-Based Augmented Reality[END_REF] is also not possible because photo books contain mostly images. Another widely used techniques to implemented AR systems are recognition algorithms such as SIFT [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF]. SIFT and similar approaches are still too demanding for today's mobile phones. Wagner et al. simplified the SIFT algorithm to make the estimation of an object's pose feasible on mobile phones [START_REF] Wagner | Multiple target detection and tracking with guaranteed framerates on mobile phones[END_REF]. Their approach is able to process camera frames with a size of 320x240 pixels at a rate up to 20Hz. They further extended the algorithm by combining it with object tracking [START_REF] Wagner | Multiple target detection and tracking with guaranteed framerates on mobile phones[END_REF]. This extension enables to recognize and track up to 6 objects with 30Hz. However, only results from processing 6 images are reported and it was not analyzed how the algorithm performs with an increasing number of objects.

In order make handheld Augmented Reality feasible for printed photo books that can contain more than 50 pages we extended the approach by Wagner et al. Similar to [START_REF] Henze | Evaluation of an Off-Screen Visualization for Magic Lens and Dynamic Peephole Interfaces[END_REF] we integrated a Vocabulary Tree [START_REF] Nister | Scalable Recognition with a Vocabulary Tree[END_REF] in the object recognition pipeline. In the pre-processing phase, photo book pages are analyzed to extract simplified SIFT features [START_REF] Wagner | Multiple target detection and tracking with guaranteed framerates on mobile phones[END_REF]. Furthermore, the photo's metadata including titles and annotated regions is converted to a XML format that describes the content of one photo book. Installed on the phone the prototype reads the content description, the according features, and scaled versions of the photos (256x256 pixels). During runtime simplified SIFT features are extracted from the images delivered by the phone's camera and compared to the SIFT features from the photo book pages using the Vocabulary Tree and bruteforce matching. If the number of matches is above a certain threshold an according homography is computed. This homography is used to draw an overlay on top of the camera image. To increase the speed, recognized pages are tracked (see [START_REF] Wagner | Multiple target detection and tracking with guaranteed framerates on mobile phones[END_REF]) in subsequent camera images. We implemented the algorithm for the Android platform using C (for the performance critical parts) and Java. The prototype recognizes objects in a 320x240 pixel camera frame with 12 FPS and tracks objects in subsequent frames with about 24 FPS on a Google Nexus One. [START_REF] Wagner | Multiple target detection and tracking with guaranteed framerates on mobile phones[END_REF] provides an extensive description of their approach and its performance, considering registration errors, and frame rate. We do not use the same implementation but we are certain that the performance is very similar.

Based on the implemented handheld AR algorithm we designed an application that provides the two visualization techniques and the three different ways to select regions. Switching between the visualizations and selection techniques is performed using a menu.

User Study

In order to compare the visualization and selection techniques developed in the previous section, we conducted a user study that is described in the following. In the experiment participants performed one task to compare the visualizations and one task to compare the selection techniques. A within-subject design with one independent variable (two conditions in the first task and three conditions in the second task) was used for both tasks.

Procedure

After welcoming a participant we explained the purpose and the procedure of the study. Furthermore, we asked for their age and noted down the participant's gender. Prior to each task we demonstrate how to use all conditions.

In the first task, participants had to answer five questions related to the photos in a provided photo book. To answer a question they had to read the augmentation shown on a mobile phone. Participants had to combine the information provided by the photos with information provided by the augmentation. E.g. one question was "Who watches soccer?". For this example participants must identify the photo with persons watching soccer and read the annotation that contains the persons' names. After answering a question participants were asked the next question. After completing all questions with one visualization technique they repeated the task with the other visualization and another photo book. We asked participants to answer the questions as fast as possible. The order of the conditions and the order of the used photo book were counterbalanced. We measured the time participants needed to answer the five questions. Furthermore, we asked them to fill the NASA TLX [START_REF] Hart | Development of NASA-TLX: Results of empirical and theoretical research[END_REF] to assess their subjective task load and the "overall reactions to the software" part of the Questionnaire for User Interaction Satisfaction (QUIS) [START_REF] Chin | Development of an instrument measuring user satisfaction of the human-computer interface[END_REF] to estimate the perceived satisfaction.

In the second task, we asked the participants to select regions on provided photos. With each of the three selection technique the participants had to mark a region in three photos (e.g. "Mark the person's face."). They could repeat marking a region if they were not satisfied with the result. Participants were asked to mark the region as fast and precisely as possible. After completing the task with one selection technique participants repeated the task with the next technique and a new set of photos. The three conditions were counterbalanced to reduce sequence effects. We measured the time needed to mark each region, the coordinates of the region, and how many attempts participants needed. Furthermore, we asked participants to fill the NASA TLX and the "overall reaction" part of the QUIS.

Participants and apparatus

We conducted the user study with 14 participants, 6 female and 8 male, aged 23-55 (M=31.21, SD=8.6). Five subjects had a technical background (mostly undergraduate students) none of them was familiar with handheld AR or the used application.

The prototype described in Section 5.3 running on a Google Nexus One was used for both tasks. The investigator selected the visualization and interaction technique between the tasks. For the first tasks we prepared two photo books printed on A4 and annotated each of the containing photos with a title and/or regions of the photo describing parts of it. The theme of the first photo book was a wedding and the theme of the second photo book was the visit to a fun fair. We prepared an additional photo book for the introduction with photos taken at a scientific conference. For the second task we printed 20 photos on A4.

Hypothesis

For the first task we predicted that the photo-aligned presentation is more usable than the phone-aligned presentation. With the photo-aligned presentation the user can see all information simultaneously and can quickly focus on different texts by changing the distance of the phone to the photo book. Therefore, we assumed that participants perceive this condition as less demanding and give it a lower NASA TLX score. Due to the same reasons we assumed that participants would give a higher QUIS score to the photo aligned presentation.

For the second task we assumed that the crosshair-based technique would receive a higher QUIS score and that this condition is perceived as less demanding, which would result in a lower NASA TLX score. We assumed that because, compared to the other conditions, the crosshair-based technique can be used with a single hand and the user can zoom and change the selection simultaneously just by moving the phone. For the touch-based techniques we assumed that unaugmented touch would be more usable because the movement of the hand does not move the image that should be selected.

Results

After conducting the experiment we collected and analyzed the data. We found significant differences between the two visualization techniques as well as between the three selection techniques. We did not find significant effects on the time participants needed to complete the tasks. Participants' qualitative feedback was translated to English.

Augmentation design

Comparing the two visualization techniques we found that the augmentation design had a significant effect (p<.05, r=0.81) on the NASA TLX score (see Figure 6). The perceived task load is lower (M=103.64) if the augmentation is aligned to the photo compared to the augmentation that is aligned to the phone's border (M=117.86). The augmentation design also had a significant effect on the participants average rating of the QUIS's "overall reactions to the software" part (p<.001, r=0.70). On average the rating is higher if the augmentation is aligned to the object (M=6.60) compared to the score for the phone-aligned visualization (M=5.36). The individual scores are shown in Figure 6. The visualization technique had a significant effect on the results of all questions (p<.001 for the first three questions and p<.05 for the others). Task completion time using a photo aligned augmentation is M=251s (SD=95s) and M=270s (SD=127s) for the phone aligned augmentation but the difference is not significant (p=0.07). Most of the participants' comments addressed the performance and the accuracy of the object recognition. E.g. one participant mentioned that "it's shaking -probably I hold the camera wrong" and another participant stated that "the recognition should be faster" and the system "should tolerate bended pages". Participants mentioned for both conditions that the recognition works better than with the other condition.

We observed for both conditions that participants prefer to hold the phone sideways. That led to negative comments about the phone-aligned presentation. E.g. "it's difficult to read because the text is skewed" or "have to turn the phone to read the text". About the photo-aligned condition participants mentioned that "it provides a good overview" and "you can see everything". However, they also mentioned that this presentation is "a bit overloaded" and "you have to go near to use the functionality".

Selection techniques

In the second task we compared the three selection techniques. We used the Bonferroni correction to correct the significance levels. The analysis of variance (ANOVA) shows that the selection technique had a significant effect on the NASA TLX score (p<.01). Comparing the individual condition (see Figure 7) shows that using unaugmented touch (M=84.07) results in a lower score than using augmented touch (M=139.36, p<.01) or the crosshair-based technique (M=161.79, p<.001). The score for augmented touch is lower than for the crosshair-based technique but, considering the corrected significance level, the effect is not significant (p=0.025).

An ANOVA test shows that the selection technique also had a significant effect on the average QUIS's "overall reactions to the software" part (p<.001). Using unaugmented touch leads to a higher score (M=6.57) compared to augmented touch (M=4.97, p<.01) or the crosshair-based technique (M=4.23, p<.001). The score for the augmented touch technique is also higher than the score of the crosshair-based technique (see the individual scores in Figure 7) but without a significant effect (p=0.027). Average task completion time for the selection subtasks are crosshair: M=5.4s (SD=4.3), augmented touch: M=6.8s (SD=5.1), and touch M=4.1s (SD=2.2) but the differences are not significant (ANOVA: p=0.08).

Even though, we demonstrated the techniques prior the task and ask the participant if he/she understands the technique, some participants did not understand the crosshair-based technique. One participants, for example, noted that "it is difficult to touch the crosshair" although it is not necessary to touch it. Mentioned reasons why this condition performs worse than the others are because it is an "unusual interaction" and that it is "difficult to mark a picture by moving the phone". Another participant noted that "moving the whole body is not comfortable". Further comments are that it is "difficult to catch the crosshair where I want it to be" and the same participants stated that "I always forget paying attention to the crosshair". An advantage participants identified is that "the finger does not occlude the object" and that this technique is "usable with one finger".

For the augmented touch technique four participants appreciated that "it has zoom" (compared to the last condition). Compared to the crosshair-based technique they liked that "one can draw the window with the finger". This condition's most often mentioned limitation is that "the device moves when dragging the box" and that "touching changes the position of the phone" or more generally: "it shakes too much for me".

We got mostly positive comments about the unaugmented touch condition. However, participants identified only one advantage of this technique, even though most participants commented on this advantage. They liked that the "image does not move" and that "the image freezed". They also explicitly stated it is "easy to select because it (the image) does not move". The main limitation the participants identified is that "it has no zoom", that "zooming would be nice" and that it is "less precise than the cursor without zoom". Another problem participants mentioned is that "my finger is to fat" or with other words "there is the fat thumb again".

Discussion

The results of the first task support our hypothesis that the photo-aligned presentation is more usable than the phone-aligned presentation. Participants perceive the photoaligned presentation as less demanding and are more satisfied. For the second task the results contradicted our hypothesis. Participants clearly prefer the unaugmented touch technique and the main reason is that image that should be selected does not move.

Based on the sparse comments we assume that the photo-aligned presentation is superior because it provides all information simultaneously and therefore helps to get an overview. The user does not have to select an object to get information about it. This is, however, also the main limitation: The photo-aligned presentation technique does not only allow the user to zoom in and out but it is required to do so. On pages with a high density of annotations the amount of text that is hardly readable can be confusing. We assume the results can be transferred to other tasks with a similar or lower object density. For those tasks the text size could be further increased, which makes it even easier to get an overview. For tasks with a considerably higher object density the text size must be adjusted accordingly to avoid overlapping texts. In this case an object aligned presentation will presumably become less usable because the user has to "zoom" often by moving the phone towards the objects.

The participants clearly preferred to select regions in a static image compared to the two techniques that use AR. This result is surprising because the design of the study favoured the two other conditions. No zoom was available even though a number of well established techniques exist to implement zooming for static images. Furthermore, we did not randomize the order of the tasks and using handheld AR in the first task certainly improved the participants' performance for the two AR based tech-niques. The qualitative feedback is also quite clear. Participants prefer unaugmented touch because they do not have to deal with the augmentation.

The study has two main limitations. The tasks and the setting are artificial in particular for the first task. For the intended use case it cannot be expected that users will search for particular information. Rather, users usually do not have temporal pressure or want to answer specific questions while browsing through a printed photo book. The second limitation, which applies for both tasks, is the short time participants used the conditions. From this perspective, it is even remarkable that all participants could use the conditions of the first task without any problems. Especially for selecting regions more training would certainly improve the performance with the AR-based techniques. However, it is questionable if training can invert the results. Furthermore, users might not be willing to learn using the crosshair-based technique because it is hard to use at least in the beginning.

Conclusions and Future Work

We investigated the design of a handheld AR interface for printed photo books. Based on an initial study we designed two visualizations for augmenting photo books with additional information. These designs and three techniques to select regions of printed photos have been implemented. The subsequent user study shows that aligning annotation to the augmented photo is more usable than aligning annotations to the phone's display. We also showed that users prefer to select regions on static images compared to selection using AR.

We assume that the results are transferable to other application domains. E.g. to interact with printed maps and text-heavy documents. Our results suggest that paperbased handheld AR systems should align text to the augmented objects even if this affects the readability. To select regions of physical objects users should not be forced to use the AR visualization. Simple selection techniques using a static image of the object is clearly preferred by the users.

If the results also apply to handheld AR for 3D objects or large objects in general needs further investigation. As next steps we propose to re-examine selection techniques for handheld AR systems. It should be investigated how more complex approaches (e.g. [START_REF] Liao | Pacer: Fine-grained interactive paper via cameratouch hybrid gestures on a cell phone[END_REF]) perform compared to traditional techniques. Furthermore, potential training effects should be studied for visualizing annotations and for select regions using handheld AR.

Fig. 1 .

 1 Fig. 1. Sketches of handheld AR interfaces by two participants. Information is either aligned to the display's top and bottom (left) or aligned to the photo (right). For illustrative purpose we selected a very simple sketch.

  Photo-aligned augmentation This design, shown in Figure2(left), attaches the information to the photos. The title of a photo is aligned to the top of the photo while the descriptions of particular parts are aligned to the top of a rectangle around this part. The augmentation follows the movement of the photo inside the camera's video. If multiple photos are visible all photos are highlighted and each has its own elements visible simultaneously.

Fig. 2 .

 2 Fig.2. The photo-aligned interface design aligns the annotations to the photos and for all visible photos simultaneously.

Fig. 2 .

 2 Fig.2. The design shows the annotations aligned to the display's top and bottom. Only the annotations of the photo below the crosshair are visible.

Fig. 3 .

 3 Fig. 3. The three techniques to select regions: crosshair-based on the left, touching in the augmentation in the centre and using touch and a static picture on the right.

Fig. 4 .

 4 Fig. 4. The three techniques to select regions: crosshair-based on the left, touching in the augmentation in the centre and using touch and a static picture on the right.

Fig. 5 .

 5 Fig. 5. The three techniques to select regions: crosshair-based on the left, touching in the augmentation in the centre and using touch and a static picture on the right.

Fig. 6 .

 6 Fig. 6. NASA TLX (left) and QUIS "overall reactions to the software" part (right) for the visualizations.

Fig. 7 .

 7 Fig. 7. NASA TLX (left) and QUIS "overall reactions to the software" part (right) for the selection techniques.

Table 1 .

 1 Frequency and rating of named information

	Information	Mentioned Rating Information	Mentioned Rating
	persons' names	9	4.2	object description	5	3.4
	recording time	8	4.3	comments	4	3.5
	recording date	7	4.7	tags/categories	3	4.0
	recording place	6	3.8	related images	3	3.5
	title/description	6	3.3	links/social net-	3	3.0
				works


Jennifer Fernquist

Garth Shoemaker
email: garths@cs.ubc.ca

Kellogg S Booth
email: ksbooth@cs.ubc.ca

"Oh Snap" -Helping Users Align Digital Objects on Touch Interfaces

We introduce a new snapping technique, Oh Snap, designed specifically for users of direct touch interfaces. Oh Snap allows users to easily align digital objects with lines or other objects using 1-D or 2-D translation or rotation. Our technique addresses two major drawbacks of existing snapping techniques: they either cause objects to "jump" to snap locations, preventing placement very close to those locations, or they "expand" motor space so that on direct-touch interfaces objects lag behind the user"s finger. Oh Snap addresses both of these problems using an asymmetric velocity profile similar to a technique for filtering degrees of freedom in multi-touch gestures that was introduced by Nacenta et al. (2009). Oh Snap applies the velocity profile to multiple "snapping" constraints. A user study revealed a 40% performance improvement over no snapping for 1-D translation, 2-D translation, and rotation tasks when snap lines or angles were targeted. We found that Oh Snap performs no worse than traditional snapping, while retaining its important functional benefits. The study also investigated optimal parameter settings and Oh Snap"s accuracy in supporting the placement of objects near to, but not at, snap locations, which traditional snapping techniques do not support. Oh Snap was found to be competitive with non-snapping interfaces for these tasks.

Introduction

Touch interfaces, such as multi-touch tabletops, interactive wall displays and mobile devices, are growing in popularity. As a result, many researchers are investigating their usefulness for completing an increasingly diverse collection of tasks, including: the control of robots [START_REF] Micire | Analysis of natural gestures for controlling robot teams on multi-touch tabletop surfaces[END_REF], the control of systems [START_REF] Bjørneseth | Dynamic positioning systems: usability and interaction styles[END_REF][START_REF] Nóbrega | Flood emergency interaction and visualization system[END_REF], managing artifacts [START_REF] Albinsson | High precision touch screen interaction[END_REF][START_REF] Aliakseyeu | Interacting with piles of artifacts on digital tables[END_REF][START_REF] Khalilbeigi | Interaction techniques for hybrid piles of documents on interactive tabletops[END_REF], and software engineering [START_REF] Frisch | Benefits of interactive display environments in the software development process[END_REF]. Most of these systems support the selection and manipulation of digital objects on the screen using direct touch, exploiting the naturalness of physical direct interaction. For example, users may touch and drag a digital robot to position its real-world counterpart, move and group digital documents, drag a 1-D slider, or rotate a dial.

Unfortunately, touch interfaces are sometimes not well suited to precise manipulation. The "fat finger problem" [START_REF] Siek | Fat finger worries: How older and younger users physically interact with PDAs[END_REF] makes selection of specific targets or placement of digital objects at precise locations or orientations difficult. Shortcomings in current sensing technology and the difficulty inherent in resolving touch contacts also contribute to the problem.

There have been several techniques developed that attempt to facilitate precise touch interactions on both large and small touch interfaces [START_REF] Albinsson | High precision touch screen interaction[END_REF][START_REF] Aliakseyeu | Interacting with piles of artifacts on digital tables[END_REF][START_REF] Benko | Precise selection techniques for multi-touch screens[END_REF][START_REF] Olwal | Spatially aware handhelds for high-precision tangible interaction with large displays[END_REF][START_REF] Olwal | Rubbing and tapping for precise and rapid selection on touch-screen displays[END_REF][START_REF] Vogel | Shift: A technique for operating pen-based interfaces using touch[END_REF][START_REF] Yang | A survey on target selection technique for touch sensing devices[END_REF]. Work has also been done to develop better methods for manipulating digital objects [START_REF] Kruger | Fluid integration of rotation and translation[END_REF][START_REF] Liu | TNT: improved rotation and translation on digital tables[END_REF][START_REF] Reetz | Superflick: a natural and efficient technique for long-distance object placement on digital tables[END_REF]. However, the fundamental issue surrounding the fat-finger problem remains. Very little has been done to improve the alignment and precise positioning of digital objects in a touch environment.

Alignment tasks in a computer interface are often assisted with "snapping" techniques [START_REF] Bier | Snap-dragging in three dimensions[END_REF][START_REF] Bier | Snap-dragging[END_REF][START_REF] Gleicher | Image snapping[END_REF][START_REF] Raisamo | A new direct manipulation technique for aligning objects in drawing programs[END_REF]. This dates back at least as far as Ivan Sutherland"s groundbreaking Sketchpad system [START_REF] Sutherland | Sketchpad: a man-machine graphical communication system[END_REF], which included snapping constraints. Snapping techniques are one of the most common object alignment methods, and are widely used in computer-aided design (CAD) and other drawing programs. Traditional snapping causes digital objects to instantaneously "jump" and then "stick" to a line or grid point that has snapping capabilities once the object is within some threshold distance from the snap location.

While this technique is sometimes sufficient for use with relative input devices, it is less suited to direct touch interfaces. Additionally, locations within the threshold area near snap locations may be inaccessible: if a user wishes to position a digital object within that area, the snapping functionality must be turned off. Toggling snapping functionality is onerous at best for single users, but it is even more of a problem on large collaborative displays, where the management of multiple widget sets and user states is a perennial problem [START_REF] Ringel Morris | TeamTag: exploring centralized versus replicated controls for co-located tabletop groupware[END_REF].

More subtle snapping techniques have been developed, such as snap-and-go [START_REF] Baudisch | Snap-and-go: helping users align objects without the modality of traditional snapping[END_REF]. These permit digital object positioning near snap locations. Snap-and-go works by expanding motor space at a snap location, resulting in objects that stop, rather than jump. However, this method is not suitable for direct touch interfaces, because it can break the correspondence between finger and object.

In this paper we describe a new snapping technique, Oh Snap, designed specifically for direct touch interfaces. We employ a technique first introduced by Nacenta et al. [START_REF] Nacenta | Separability of spatial manipulations in multi-touch interfaces[END_REF]. Our technique is designed to support quick snapping to any one of a set of lines, angular orientations, or other constraints, while still allowing objects to be positioned in close proximity to one another and while maintaining a close correspondence of the user"s finger with the dragged object throughout. This set of benefits is unique to our technique. Oh Snap provides a subtle snapping effect that needn"t be explicitly enabled or disabled. It avoids limitations in existing alternatives and thus facilitates placements that other techniques do not. We compare our work to the earlier work by Nacenta et al. [START_REF] Nacenta | Separability of spatial manipulations in multi-touch interfaces[END_REF] in more detail after describing the new technique and a two-phase user study that was conducted to assess our technique.

Related Work

Many researchers have investigated approaches for supporting direct manipulation with objects on touch surfaces. Wobbrock et al. [START_REF] Wobbrock | User-defined gestures for surface computing[END_REF] investigated user-defined gestures for general interactions on multi-touch tabletops and found that touching-and-dragging is the most natural method for translating digital objects, and that dragging by the corner is the most natural way to rotate objects. Similarly, Micire et al. [START_REF] Micire | Analysis of natural gestures for controlling robot teams on multi-touch tabletop surfaces[END_REF] conducted an analysis of user-defined gestures for robot manipulation on a multi-touch tabletop. They too found that touch dragging was the most used gesture for positioning robots. Kruger et al. [START_REF] Kruger | Fluid integration of rotation and translation[END_REF] and Liu et al. [START_REF] Liu | TNT: improved rotation and translation on digital tables[END_REF] developed additional methods for performing fluid rotation and translation of objects using direct touch.

Many tasks designed for a touch interface can benefit from precise positioning of digital objects. Nóbrega et al. [START_REF] Nóbrega | Flood emergency interaction and visualization system[END_REF] created LIFE-SAVER, an interactive visualization system for a touch interface to analyze emergency flood situations. Studies by Bjørneseth et al. [START_REF] Bjørneseth | Dynamic positioning systems: usability and interaction styles[END_REF] used a touch table for dynamic positioning of maritime equipment. This safety-critical task requires careful translation and rotation for specifying vessel positioning and heading, respectively.

The "Fat Finger" Problem

Many touch devices capture a touch contact "point" that is actually a relatively large 2-D region [START_REF] Siek | Fat finger worries: How older and younger users physically interact with PDAs[END_REF]. This is often converted into a single (x,y) pixel coordinate for compatibility with traditional pointing models that assume a single point of interaction. However, there is no guarantee that this single point is the true contact point intended by the user. Despite many advances in technology, this problem persists in part due to a lack of sophisticated sensing techniques, but also because the intended point of interaction is inherently ambiguous. Techniques such as "focus+context" lenses have been designed to help mitigate this problem within information visualization applications [START_REF] Voida | Getting practical with interactive tabletop displays: designing for dense data, "fat fingers," diverse interactions, and face-to-face collaboration[END_REF], but no general solution exists for all types of applications.

Sensing limitations may give rise to a variety of issues when manipulating objects, such as unintentional movement of the object. For example, when a finger lifts up from the screen its contact area changes shape, which may result in a change to the calculated pixel coordinates. If a user were attempting to precisely place a digital object, this might cause the object to shift from the desired target.

There has been work to resolve the fat finger problem by obtaining a more accurate touch contact point [START_REF] Holz | The generalized perceived input point model and how to double touch accuracy by extracting fingerprints[END_REF][START_REF] Wang | Empirical evaluation for finger input properties in multi-touch interaction[END_REF], providing feedback to the user about the success/failure of the touches [START_REF] Wigdor | Ripples: utilizing per-contact visualizations to improve user interaction with touch displays[END_REF], and incorporating selective zooming [START_REF] Vogel | Shift: A technique for operating pen-based interfaces using touch[END_REF]. Although these techniques can provide a more accurate touch contact location, they do not assist substantially in object alignment tasks.

Existing Snapping Techniques

Traditional snapping techniques, such as snap-dragging [START_REF] Bier | Snap-dragging[END_REF], cause objects to automatically jump to snap locations once they are within a predefined distance from the snap location. Basic snapping is highly effective in assisting alignment tasks; however, while it is sometimes sufficient for use with relative input devices, such as computer mice, it is less well suited to direct touch interfaces. It is highly unintuitive if an object a user is touching suddenly jumps around underneath the user"s finger.

Worse yet, traditional snapping does not support the placement of objects near to, but not exactly at, snap locations. Disabling snapping can address this problem, but explicitly toggling snapping is highly undesirable for a touch interface, especially in a collaborative environment. The snap toggling function would either have to be a global function, affecting all users, or a local function, which would require additional information to determine the identity of the activator. It might also have to be placed in one or more menus, accessible to all users, or activated with a possibly complex gesture, requiring at least some additional training of users [START_REF] Gutwin | Design for individuals, design for groups: tradeoffs between power and workspace awareness[END_REF].

Snap-and-go, introduced by Baudisch et al. [START_REF] Baudisch | Snap-and-go: helping users align objects without the modality of traditional snapping[END_REF], is a snapping technique that does not require toggling on or off. It functions by expanding motor space at snap lines, resulting in objects stopping at the desired location as opposed to automatically jumping there once they are within some threshold distance. This allows objects to be placed near snap lines as well as directly on them, unlike traditional snapping.

Snap-and-go works well for relative input devices, such as mice. With relative devices, snap-and-go stops a dragged object at the snap line as the user keeps moving the mouse beyond the snap line. After a short distance, the object begins moving with the mouse again. Unfortunately, snap-and-go is not suitable for direct touch interfaces where the correspondence between a user"s finger and an object under manipulation should ideally be maintained at all times. If a user were to drag an object across a snap-and-go line, the finger would permanently move out ahead of the object. Indeed, the more snap lines an object crosses, the farther the object would lag behind the finger that was dragging it, in effect "losing" any direct object-finger correspondence.

There have been other attempts to solve these problems. Pseudo-haptic feedback has been used to improve interactions with graphical user interfaces, causing screen widgets to "feel" sticky, magnetic, or repulsive [START_REF] Lecuyer | Simulating haptic feedback using vision: A survey of research and applications of pseudo-haptic feedback[END_REF]. Researchers have developed sticky widgets [START_REF] Mandryk | Sticky widgets: pseudo-haptic widget enhancements for multi-monitor displays[END_REF] and "force fields" [START_REF] Ahlström | An evaluation of sticky and force enhanced targets in multi target situations[END_REF] to help with window alignment in mousebased environments. These ideas could be adapted to the translation and alignment of objects on touch tables; however, they would suffer from the same drawbacks as snap-and-go, because the finger could "lose" the object. The problem remains of maintaining a close correspondence between a user"s finger and the object that is being manipulated.

The "Oh Snap" Technique

Oh Snap is a snapping technique designed specifically for touch interfaces. It possesses several benefits, including: it eases alignment of objects with snap lines, it doesn"t require toggling modes, it maintains the correspondence of finger to object, and objects can be placed close to snap lines or other objects without snapping interfering.

The basic idea behind the Oh Snap technique is shown in Fig. 1. To begin, a user drags an object as she normally would until the object first touches a snap line, at which point the object stops moving even if the user does continue to drag her finger. The object remains stationary unless the user"s finger travels a small distance (the snap-width) beyond where snapping has occurred. Once the finger travels beyond the snap-width, the object starts moving at a rate faster than the finger is moving. Once the finger travels further, beyond the catch-up width, the object will have caught up to the finger, and dragging continues as usual. Of course, if the user lifts her finger while the object is snapped to the line, the object remains in its aligned position.

Fig. 1.

As a finger moves an object downward to a snap line (a) the object "snaps" when its leading edge touches the snap line (b). As the finger continues downward, the object remains snapped to the line (c). When the finger is beyond the snap width, the object un-snaps and starts catching up to the finger (d). When the finger reaches snap width + catch-up width the object has returned to its original position relative to the finger (e).

During the catch-up phase an object travels at a rate faster than the finger. The motion of an object in the catch-up phase is defined by a linear interpolation that calculates the object"s position proportional to the finger"s position within the catchup region. Pseudo code for the algorithm is shown in Fig. 2. The number of pixels the object travels for each pixel the finger travels is determined by the ratio calculated in Eqn. 1, which is shown on the left in Fig. 3. This is the rate at which an "un-snapped" object catches up to the finger. The ratio can also be considered to be the size of a super pixel relative to a real pixel, the distance moved by the object each time the user"s finger moves one real pixel. linearInterpolation(fingerX, fingerOriginX, snapWidth, catchUpWidth) { return fingerOriginX + (fingerX-fingerOriginX-snapWidth) * (snapWidth + catchupWidth) / catchUpWidth; } Fig. 2. Code fragment for the linear interpolation function that returns the position of the object moving in the x direction if the object is snapped and the finger position is in the "catch-up" area. In this code fingerX is the current finger position, and fingerOriginX is the position the finger was in when snapping occured.

A temporal diagram of the Oh Snap technique is shown in Fig. 3. The graph shows how an object first travels normally, how it then snaps to a line while the user"s finger is in the snap region, and how it eventually catches back up to the finger because it travels faster than the finger in the catch-up region.

Snap width and catch-up width

The ratio in Eqn. 1, and thus the size of the super pixels, must be carefully chosen. When the catch-up width is very large, the ratio approaches one so objects effectively never catch up. Conversely, when the catch-up width is close to zero, the ratio approaches infinity and objects jump to their original position underneath the user"s finger as soon as they un-snap. This can make it difficult to position an object at a location closer than the snap width to a snap line. Ideally, the magnitude of catch-up width + snap width should be less than the width of an average touch contact. Wang and Ren [START_REF] Wang | Empirical evaluation for finger input properties in multi-touch interaction[END_REF] found this to be 36 pixels (0.4mm/pixel) for an oblique touch from an index finger. The snap width should be large enough to accommodate users overshooting a target, otherwise objects un-snap before the user"s finger has a chance to stop. A balance must be struck so that catching-up is imperceptible but still occurs quickly enough to be useful. If the ratio is too close to one, some positions near a snap line can be unreachable, due to quantization effects, unless the touch sensors provide sub-pixel resolution. For example, if the ratio (and super pixel size) is 2, a position 3 pixels away from a snap line is unreachable, because the object will be moving in steps of 2 pixels as the finger moves in steps of 1 pixel. This can be mitigated either by lifting the finger right after crossing the snap line, which flags the object as un-snapped, and then putting it down again and resuming with normal dragging, or dragging the object far enough away from, and then back towards, the snap line. Neither seems like a very good solution, which emphasizes the need for proper selection of catch-up and snap widths. We discuss the selection of these parameters in section 5.2 and evaluate three different parameter sets in the second phase of our user study.

Benefits of the Oh Snap technique

The benefits of Oh Snap are summarized in Table 1. First, Oh Snap preserves the position of the user"s touch point on a digital object relative to that object. This feature is especially useful if users drag objects across snap lines when they have no intention of aligning the object to those lines. Although the object will temporarily snap to those lines as it crosses them, the object will eventually catch up with the user"s finger and return to its original relative position underneath it. This is crucial for touch tables or other direct touch interfaces. Second, Oh Snap allows users to place digital objects near snap lines as well as align them with snap lines without having to toggle the snap capabilities on and off. This is important in collaborative environments where toolbars that may hold the snap toggle might not be accessible (reachable) by some users. Lastly, because Oh Snap supports all object positioning tasks, there is no need to incorporate mode switching functionality into the interface.

Fig. 4.

Code fragment for the 1-D Oh Snap function for the case when the object is moved from left to right (lower to higher X coordinate). In this code objectBorder is a variable that references the boundary of the object and rightX is the x position of its rightmost edge. Additional cases are implemented similarly.

We implemented Oh Snap in C#, running on a SMART Table. We implemented three object movement types that are frequently performed on touch tables: 1-D translation, 2-D translation, and rotation. Rotation has been shown to be very useful on touch tables, especially in collaborative situations [START_REF] Kruger | How people use orientation on tables: comprehension, coordination and communication[END_REF]. For translation, the position of each edge of an object is checked against the position of all environment lines it is parallel to. Orthogonal edges in the 2-D environment can be snapped independently. For rotation, the snap width is measured as an angle rather than as a pixel count.

If a user snaps an object to a line and then lifts her finger from the screen, the object is no longer flagged as being snapped, whether it is aligned with the line or is within the (snap width) + (catch-up width) region. This is primarily useful if a user wishes to place an object near a snap line but has snapped the object and is having difficulty reaching the destination due to the object moving along super pixels. A code snippet for the 1-D Oh Snap function that handles an object moving from left to right is presented in Fig. 4. This uses the linear interpolation function in Fig. 2.

User Study

In order to objectively evaluate the performance of the Oh Snap technique, we performed a user study. The first phase focused on comparing the performance of Oh Snap to traditional snapping and to no-snapping for alignment tasks. The second phase investigated participants" ability to drag an object close to but not onto a line (i.e. to not snap the object to the line) when the Oh Snap feature is turned on.

Comparison of Snapping Techniques -Phase 1 of the User Study

The purpose of this phase of the user study was to evaluate the Oh Snap technique and compare its performance to traditional snapping as well as to no snapping. We investigated performance with three object movement types: 1-D translation, 2-D translation, and rotation.

Task. A participant"s task was to use a single finger on the right hand to move a digital blue square, as quickly as possible, so that its edge(s) were aligned with the desired target line(s). The target lines were indicated with a black arrow, which turned green when the object was aligned with the indicated line. When all edges were aligned correctly (1 edge for the 1-D and rotation tasks, 2 edges for the 2-D task), the square also turned green, indicating success. Screenshots of the start and end of successfully completed 1-D, 2-D, and rotation tasks are shown in Fig. 5.

At the start of a trial, a button at the bottom of the screen labeled "GO" became active after one second. When the active button was touched, the button disappeared, the square became active (indicated by its changing color from grey to blue), and the participant could then begin the trial. The timer began when the participant first touched the square and it stopped when the participant lifted the finger from the screen with the appropriate edge(s) of the square aligned to the target line(s). Each trial required successful alignment. If a participant lifted the finger when the square was not fully aligned, the participant would have to touch the square and move it again to complete the trial.

The layouts of the three tasks were designed so that their components occupied the lower portion of the screen. This allowed participants to sit at the table and comfortably reach the digital objects. For the 1-D translation task, participants moved the square so that its right edge was aligned with a single target line. The right edge of the square began at a distance 305 pixels (17cm) to the left of the target line. In the 2-D translation task, the square was to be aligned so that its top edge and left edge were aligned to horizontal and vertical target lines, respectively. At the start of a trial, the top left corner of the square was 490 pixels (27cm) from the intersection corner of the target lines. In the rotation task, participants rotated a rectangle, anchored at one end, so that the central line protruding from it was parallel to the target line. The target line was horizontal and the anchored rectangle began vertical at the start of a trial, rotated 90 degrees from the target line. A pilot study showed that crossing multiple distracter snap lines did not affect performance time. To simplify instructions to participants we did not use distracter lines in the final version of the study. Interface techniques. There were three snapping interface conditions: no snapping, Oh Snap, and traditional snapping. Trial tasks were identical across conditions, but the snapping behaviour of the target lines differed. In the no snapping condition, the target line did not cause objects to snap, and the edge of the square had to be placed within +2 pixels of the appropriate target line(s). Pilot testing revealed that the no snapping tasks were nearly impossible to complete without a small tolerance due to the touch sensing limitations of the SMART Table and the fat finger problem.

In the traditional snapping condition, the target lines had the traditional snapping behaviour with a snapping threshold of 10 pixels so that if the appropriate square edge was within that threshold of the target line, it would automatically be translated to the target line position. In the Oh Snap condition, the target lines had the Oh Snap behaviour with a snap width set to 10 pixels and the catch-up width set to 10 pixels. To conduct a fair comparison, we gave Oh Snap and traditional snapping the same threshold to make them as equivalent as possible.

Experimental Design. The study was a fully counter-balanced, within subjects 33 (Snapping Technique  Movement Task) design with 20 trials for each treatment. For every trial, we recorded the task completion time and the number of times participants lifted a finger from the table (referred to as a touch up).

Participants were given training and the opportunity to practice each movement task (with no snapping) at the start of a session. For data analysis, we discarded the first 5 trials of each treatment of 20 trials to reduce learning effects.

Participants. Eighteen right-handed participants (2 female) between the ages of 21 and 40 ( = 26.7) with normal color vision were recruited from our institution. Six participants had previously used a tabletop display, but only briefly during demos or to play games. Each participant received $10 for participating. Prior ethical approval was obtained from our university"s behavioral research ethics board.

Apparatus. The user study was conducted on a SMART Table from SMART

Technologies. The table had a 57.2cm × 42.9cm screen, with a resolution of 1024 × 768 pixels, and a 70Hz refresh rate. The application used for this user study was written in C# using the SMART Table SDK.

Hypotheses. We had two hypotheses: (1) Participants would perform faster with Oh Snap than with no snapping, and (2) participants would not perform slower with Oh Snap compared to traditional snapping. We were also interested in seeing how snapping technique would impact touch ups, the number of times users lift a finger from the table during a task.

Results for Phase 1. We performed a repeated measures ANOVA on the dependent variable trial time. A Bonferroni adjustment was applied to all pair-wise comparisons. Mean trial times for all snapping techniques across all movement types are shown in Table 2. Performance. As shown in Table 2, the average trial time in seconds was 2.507 for no snapping, 1.513 for Oh Snap, and 1.387 for traditional snapping. There was a significant main effect of snapping technique (F(2,34)=30.062, p<.0005,  2 =.639) and movement type (F(2,24)=27.529, p<.0005,  2 =.618). There was a significant interaction effect of snapping technique  movement type (F(4,68)=9.493, p<.007).

We ran pair-wise comparisons between techniques and found that both Oh Snap (p<.0005) and traditional snapping (p<.0005) were faster than no snapping, but traditional snapping was not significantly faster than Oh Snap. We also ran pair-wise comparisons on movement types and found significant differences between all pairs: 1-D and 2-D (p<.005), 1-D and rotation (p<.0005), and 2-D and rotation (p<.0005).

To understand how snapping technique impacted performance for each movement type, we ran a repeated measures ANOVA for each of the three movement types. There was a significant effect of snapping technique for rotation (F(2,16)=17.634, p<.0005,  2 =.688), 1-D translation (F(2,16)=14.411, p<.0005,  2 =.643), and 2-D translation (F(2,16)=10.128, p<.001,  2 =.559). Both Oh Snap and traditional snapping were significantly faster than no snapping for all movement types (p<.0005). Traditional snapping was significantly faster than Oh Snap only in the 1-D task (p<.01); however, it was only 14% faster whereas Oh Snap was 31% faster than no snapping in the 1-D task. A chart showing the mean trial times for each movement type grouped by snapping technique is in Fig. 6. Number of touch-ups. We also conducted a 3×3 (Snapping Technique × Movement Task) repeated measures ANOVA for the average number of touch ups in a set of trials. There was a significant main effect of both snapping technique (F(2,34)=21.427, p<.0005,  2 =.558) and movement task (F(2,34)=9.45, p<.001,  2 =.357). Pair-wise comparisons showed that participants lifted their finger up significantly more in the no snapping condition (μ=1.438) compared to either Oh Snap (μ=1. [START_REF] Micire | Analysis of natural gestures for controlling robot teams on multi-touch tabletop surfaces[END_REF] or traditional snapping (μ=1.185) with p<.0005 for both. There was no significant difference in the number of touch ups between Oh Snap and traditional snapping. There was a significant interaction effect for snapping technique × movement type (F(4,68)=6.274, p<.023).

Questionnaire data. Questionnaires were administered after each snapping technique to collect subjective ratings and receive comments using TLX-based Likert-style questions. Pair-wise comparisons using Wilcoxon Signed Ranks Tests for each of the questionnaire questions are presented in Table 3.

Participants ranked the three snapping techniques from best to worse in terms of preference. A Friedman test showed that technique significantly impacted rankings  2

(2,N=18) =10.371, p<.006). Both Oh Snap and traditional snapping had a mean rank of 1.67 while no snapping had a mean rank of 2.56 (lower is better).

Discussion of Phase 1. Both the Oh Snap and the traditional snapping techniques were significantly faster than no snapping, supporting hypothesis (1). On average, Oh Snap was 40% faster than no snapping. Because traditional snapping was not significantly faster than Oh Snap on average, hypothesis (2) is also supported. As reported in the results, although traditional snapping was significantly faster than Oh Snap in the 1-D tasks, it was only 14% faster, but Oh Snap was 31% faster than no snapping for such tasks. This small degradation suggests that Oh Snap is a reasonable alternative to traditional snapping for alignment tasks on touch interfaces, and it has the very important added benefit that users can place objects in close proximity to snap locations. Table 3. Summary of significance differences between snapping techniques for each post-condition questionnaire question. A=Oh Snap, B=No snapping, and C=Traditional snapping. Non-significant comparisons are labeled n.s. Although this phase of the user study showed that Oh Snap performs well for alignment tasks, it did not investigate how Oh Snap might benefit proximity placement. To properly assess this feature, we conducted a second phase of the user study to compare several variations of the snap width and catch-up width parameters to find the best values for each.

Significant differences between snapping techniques in subjective answers

Question

Parameters for Snap Line Proximity -Phase 2 of the User Study

This phase investigated the variation of the Oh Snap parameters snap width and catch-up width, and measured their effect on user performance for positioning digital objects in close proximity with (but not aligned to) a snap line. We compared Oh Snap variants to the no snapping technique. We hoped to find a balance between a small ratio (Eq. 1) to reduce super pixel size, and minimizing the sum catch-up width + snap width so that it was not much larger than the average touch contact width.

Apparatus and Participants. The apparatus and the participants were the same as in Phase 1. Participants started Phase 2 shortly after they completed Phase 1.

Task. The task was very similar to the 1-D alignment task from Phase 1. Participants were asked to translate a square so that it was close to, but not quite aligned with, a snap line. A dotted target line, with an arrow pointing to it, was placed 5 pixels before or 5 pixels after the Oh Snap line. Participants were asked to drag the square so that its right edge was aligned with the dotted target line. As in Phase 1, once the square was aligned with the target line, both the arrow and the square turned green to indicate successful alignment.

A screenshot of the start of each of the tasks (after the "GO" button had been pressed) is shown in Fig. 7.

Interfaces. There were four sets of snapping parameters.

 no snapping  a 4/3 ratio with 15px catch-up width and 5px snap width  a 3/2 ratio with 20px catch-up width and 10px snap width  a 4/3 ratio with 30px catch-up width and 10px snap width Traditional snapping was not used because the target lines were within the traditional snapping threshold area and thus impossible to reach with that technique.

We chose these parameter sets based primarily on our findings during pilot testing, when we found that ratios of less than 3/2 made it difficult to accomplish proximity tasks. We chose 40 pixels, or 2.24cm on the SMART Table, as the largest catch-up width + snap width to investigate if a width larger than the average touch contact width of 1.44cm [START_REF] Wang | Empirical evaluation for finger input properties in multi-touch interaction[END_REF] would yield positive results. A large value for catch-up width + snap width allows for tenacious snapping of an object"s position while also maintaining imperceptibility, both of which are desirable. If that size gave good results, future Oh Snap implementations would have to take great care not to overlap the snap and catch-up regions of different snappable lines.

Experimental Design. The study was a partially counter-balanced, within subjects 4×2 (Snapping Parameter Set × Proximity Task) design with 20 trials for each treatment. For each trial, we recorded the task completion time. As in Phase 1, participants were given training and the opportunity to practice each movement task (with no snapping) at the start of a session. For the purposes of data analysis, we discarded the first 5 trials of each treatment of 20 trials to reduce learning effects.

Hypotheses. We had two hypotheses: (3) The Oh Snap conditions would be no slower than the no snapping condition, and (4) participants would perform fastest with the largest catch-up-size-to-snap-width-ratio and the largest snap-width. When the ratio large (i.e. the catch-up size is 3 times larger than the width) the size of super pixels is decreased so it should be easier to perform fine-grained movement of a digital object that has been snapped. We expected that participants would perform better placing objects just before the snap line than just after it. Because objects could avoid being snapped when placing them before the snap line, this proximity task would be less difficult.

Results for Phase 2. We ran a repeated measures ANOVA on the dependent variable trial time. A Bonferroni adjustment was applied to all pair-wise comparisons. Mean trial times for all Oh Snap parameter sets across both proximity tasks are presented in Table 4.

Performance. There was a significant main effect of Oh Snap parameter set (F(3,51)=12.702, p<.0005,  2 =.428) and proximity task (F(1,17)=6.626, p<.02,  2 =.28). Mean trial times for all parameter sets are presented in Fig. 8. Pair-wise comparisons revealed that the no snapping condition was significantly faster than all parameter sets (p<.0005 for the first, p<.002 for the second, p<.0005 for the third). Participants did not complete trials significantly faster with any Oh Snap parameter set compared to any other. There was a significant interaction effect of parameter set × proximity task (F(3,15)=3.884, p<.031). Mean trial times for each proximity task type grouped by Oh Snap parameter set are shown in Fig. 8.

Surprisingly, the proximity task that required participants to position the square just before the snap line took significantly longer than positioning the square just after the line (p<.02). We anticipated that because the objects could avoid being snapped when placing them before the snap line, this proximity task would be less difficult and faster as a result. In sharp contrast, squares positioned after the snap line must be snapped and therefore we thought they would take longer. Perhaps participants knew that snapping would occur in the "after" task and learned how to adjust their movements to accommodate it. Conversely, in the "before" task, participants may have worked slower that they did not snap to the line. Participants may have occasionally overshot the target position, though not always, resulting in inconsistent behaviour preventing them from mastering the task.

Number of touch ups. We conducted a 4 × 2 (Parameter Set × Proximity Task) repeated measures ANOVA for the number of touch ups in each treatment. There was a significant main effect of both parameter set (F(3,51) = 6.79, p<.001,  2 =.285) and proximity task (F(1,17)=5.236, p<.035,  2 =.235). Pair-wise comparisons of Oh Snap parameters revealed the no-snapping condition had significantly fewer touch ups than the second (p<.002) and fourth (p<.008) parameter sets, but not the third.

Discussion of Phase 2. Hypotheses (3) and (4) were not supported: the no snapping condition was faster than the three Oh Snap conditions and there were no significant differences in mean trial time between the parameter sets. While this means that there is a price, in terms of raw speed, for using Oh Snap, if snapping is desired the nosnapping condition is not a viable option. Oh Snap"s real competitors are other snapping techniques. The advantage it has is that it permits placement of objects at any location, including right near a snap line. There is very little penalty for doing this compared to no snapping, and it cannot be done at all with other techniques.

There was no difference between the number of touch ups in the no snapping condition and in the condition using the third Oh Snap parameter set (3/2 ratio, 10px snap width). We recommend those parameters for future Oh Snap implementations.

Conclusions & Future Work

We introduced and evaluated a novel snapping technique supporting the alignment of digital objects on a touch interface. Oh Snap maintains the position of objects underneath the user"s finger. It allows placement of objects close to snap lines, without having to toggle the snap capabilities off and on. This feature is especially important for collaborative environments. Oh Snap offers a mean 40% performance improvement over no snapping for 1-D, 2-D and rotation tasks, and it does not perform significantly worse than traditional snapping for those tasks on a touch table.

We investigated three parameter sets for Oh Snap in two proximity placement tasks. The no snapping condition performed better than the parameter sets, but the differences were not very large. Placing objects near a snap line is an infrequent task compared to snapping objects or placing objects in open space, both of which are tasks at which Oh Snap excels. We argue that, for direct touch surfaces, Oh Snap is superior to no snapping or traditional techniques because of its unique combination of attributes (Table 1). Furthermore, the lack of a significant difference between parameter sets for Oh Snap indicates that there is probably a wide range of suitable values, meaning that developers can choose values that best suit their users" needs.

Comparison to Nacenta et al.'s prior work

The Oh Snap velocity profile is similar to the "magnitude filtering" used by Nacenta et al. [START_REF] Nacenta | Separability of spatial manipulations in multi-touch interfaces[END_REF] to block selected degrees of freedom. In their approach velocity profiles are used to determine whether motion is the beginning of a constrained gesture or if it is "noise" in the current gesture. In contrast to this, Oh Snap employs the profile not just when movement is initiated, but whenever a snap line is encountered. Oh Snap uses the velocity profile within a chosen degree of freedom (1-D or 2-D translation, or rotation) to decide when to stop, whereas Nacenta et al. use it to change or start a chosen degree of freedom.

Nacenta et al. use symmetric constraint regions. Our regions are asymmetric; no snapping occurs approaching a snap line, only after it is passed. This makes it less likely a target just ahead of a snap line will be "missed." If a target just after a snap line is missed, "backing up" will not be handicapped because the asymmetry means that no snapping is applied in the reverse direction once the object becomes unsnapped.

Extensions to Oh Snap

Oh Snap works equally well on a traditional single-touch table or on a multi-touch table. If multi-touch is available, the number of touches could be used as a mode switch, which would allow seamless transitions between snapping and no-snapping. Future work for these and other snapping techniques will involve a new bimanual Oh Snap interaction that does not require explicit implementation of system-defined snap lines. An Oh Snap user will touch the interface with two fingers of the same hand and which define a temporary snap line to which they can align an object.

1 )Fig. 3 .

 13 Fig. 3. The relationship of object translation relative to finger translation as an object moves, first normally, then snapped (stationary) in the snap region, and eventually catching up to the finger again as it leaves the catch-up region.

(a) 1 -Fig. 5 .

 15 Fig. 5. Screenshots of the three Phase 1 tasks at the start and end of a trial.

Fig. 6 .

 6 Fig. 6. Mean trial time by movement type grouped by snapping technique. Error bars represent standard error.

  (a) Target line is before snapping line. (b) Target line is after snapping line.

Fig. 7 .

 7 Fig. 7. Screenshots of each of the two tasks at the start of a trial in Phase 2.

Fig. 9 .

 9 Fig. 9. Mean trial time by proximity type grouped by Oh Snap parameter set. Error bars represent standard error.

Table 1 .

 1 Comparison of snapping techniques.

	Technique	Fast snapping Mapping maintained Close placement
	Oh Snap	Yes	Yes	Yes
	snap-and-go	Yes	No	Yes
	traditional	Yes	Yes	No
	no snapping	N/A	Yes	Yes
	4 Implementation
	OhSnap(objectBorder, snapLineX, snapWidth, catchUp){
	if (objectBorder.rightX == snapLineX && !isSnapped){
	isSnapped = true;
	fingerPositionSnapped.x = currentFingerPosition.x;
	}
	if (isSnapped){
	fingerDiff = currentFingerPosition.x -
		fingerPositionSnapped.x;
	if (fingerDiff <= snapWidth)
	return snapLineX;
	else if (fingerDiff > snapWidth &&
		fingerDiff <= snapWidth + catchUp)
	return linearInterpolation(
		fingerPositionSnapped.x, currentFingerPosition.x,
		snapWidth, catchUp);
	else
	isSnapped = false;
	}
	return objectBorder.rightX;
	}

Table 2 .

 2 Mean trial time (in seconds) for each of the snapping techniques across all movement types.

	Snapping Technique	All	Movement Type 1-D 2-D	Rotation
	No snapping	2.507	2.301	3.517	1.703
	Oh Snap	1.513	1.582	1.667	1.287
	Traditional snapping	1.387	1.362	1.651	1.147

Snapping Technique A better than B C better than A C better than B

	Mental demand	.013	n.s.	.003
	Physical demand	.033	n.s.	n.s.
	Task difficulty	.005	.021	.001
	Success	.03	n.s.	.007
	Hard work	.003	n.s.	.003
	Fulfilled	.001	n.s.	.0005

Table 4 .

 4 Mean trial time (seconds) for each of the Oh Snap parameter sets across both proximity types (just before and just after a snap line).

snap width catch-up width Ratio Proximity Type Both Before

						After
	No snap	No snap	N/A	2.309	2.236	2.382
	5px	15px	4/3	3.350	3.689	3.012
	10px	20px	3/2	3.559	3.919	3.198
	10px	30px	4/3	3.662	4.128	3.196

Acknowledgements

This research was funded in part by the Networks of Centres of Excellence of Canada through GRAND, the Graphics, Animation and New Media NCE, by the Natural Sciences and Engineering Council of Canada under the postgraduate scholarship program and the discovery grants program, by the University of British Columbia through a University Graduate Fellowship, and by an equipment donation from SMART Technologies. Defense Research and Development Canada supplemented the NSERC postgraduate scholarship. Additional equipment and other infrastructure necessary for the research were provided by funding from the Canadian Foundation for Innovation and the British Columbia Knowledge Development Fund.