Leila Schemali

Jean-Marc Thiery

Tamy Boubekeur

C Andujar

E Puppo

Leïla Schemali

Automatic Line Handles for Freeform Deformation

published or not. The documents may come

Introduction

Interactive freeform deformation (FFD) tools allow to edit easily the shape of a surface mesh through a high level control structure. Typical examples of such structures include cages, skeletons and handle sets. We focus on the later, for which a number of recent variational techniques have been introduced. The basic interface for all these techniques is usually the same: the user selects a region of interest (ROI) which will be deformed while the remaining part of the object stays static. The ROI is decomposed in two components: a rigid motion handle, manipulated explicitly by the user, and an influence area, interpolating the handle and the static part boundaries smoothly. The critical computation workload is usually dedicated to this smooth interpolation, for which we seek for feature preservation under rotation and/or translation invariance. To achieve such a behavior, non-linear [BPGK06, SA07] and linear techniques [BS08] offer a performance gradient ranging from interactive to real time for several tens or hundreds of thousands of vertices.

With these powerful deformation tools in hands, one of the main challenges remains the definition of the handles, which is a tedious task even for expert users. We can classify the type of handles in two categories. First, handles aiming at introducing new geometrical structures on a base surface: stemming from the user wishes, they may take place in flat areas and cannot be inferred automatically. Second, handles capturing existing components that the user wants to modify. Here, we argue that important geometric components on a surface can be faithfully captured with a set of lines defined at its most salient features. Indeed, this is a phenomenon explored by line drawing algorithms to convey shape expressiveness with minimal data. Therefore, we propose an automatic handle generation process for freeform deformation based on feature lines which provides controllable line handles and their associated influence area dynamically during mesh editing. The resulting FFD interface is view-dependent and allows to quickly edit the shape by simply grabbing and moving line handles. As the deformation is entirely performed through a constrained generic variational framework [LSLCO05], the user can switch back to classical manual mode at anytime e.g., adding individual constraints. Beyond the system, we make two technical contributions:

• a regularization procedure which converts feature lines to line handles; • a supervised ROI generation algorithm based on a fast diffusion process.

As a result, our system provides automatic handles fitting important characteristics of the shape (e.g., main anisotropic lines, contours, etc) and providing an intuitive modeling interface for arbitrary 3D surface meshes.

Background.

Shape Editing with Line Handles. Wires [START_REF] Singh | Wires: a geometric deformation technique[END_REF] allows to place lines on a shape and to transfer the deformation from the lines to the mesh. Lines are placed manually on the surface and offer a great flexibility to design a complete modeling structure on top of the surface, less restrictive than cages for instance. However, lines have to be modeled manually.

The SilSketch [ZNA07, NSACO05] algorithm allows the user to "over-sketch" silhouettes of an object. The system finds the closest silhouette correponding to the sketched line and deforms the surface using Laplacian deformation so that its actual silhouette fits the sketched line. This system allows to quickly redesign the global outline of a shape, but is restricted to interior and exterior contours and does not allow to control the area of influence of the resulting deformation. FiberMesh [START_REF] Nealen | Fibermesh: Designing freeform surfaces with 3d curves[END_REF] proposes to create a mesh by drawing 2D lines. Improving over existing sketch-based modeling tools such as Teddy [START_REF] Igarashi | Teddy: a sketching interface for 3d freeform design[END_REF], the surface is defined through a variational framework from positional and curvature constraints located at the sketched lines. The generating lines can be edited to deform the shape and new lines can also be inserted dynamically. However, all lines must be explicitely drawn by the user.

The closest approach to ours is the system by Zhou et al. [START_REF] Zhou | Feature-based mesh editing[END_REF] which uses ridges and valleys to define handles from which deformation and curvature can be edited. This method allows to edit CAD meshes easily, but is not adapted to arbitrary shapes. Moreover, many view-dependent salient structures cannot be captured by curvature lines.

Finally, [START_REF] Attene | Hierarchical convex approximation of 3d shapes for fast region selection[END_REF] detects important features by constructing a hierarchical structure representing the 3D shape and uses it to select features at different scales. However, the proposed features are static and do not adapt to the viewpoint.

Linear Rotation-Invariant Deformation. Although our approach is compatible with any handle-based deformation technique [SA07, BS08], we choose the linear rotationinvariant (LRI) coordinates method [LSLCO05] as the underlying deformation machinery for our experiments. In particular, this method is linear and guarantees a unique surface reconstruction from positional constraints, with a good trade-off between speed and quality. LRI encodes geometry as differential coordinates between vertices and user deformation as positional constraints, resulting in an overdetermined system, solved in the least-squares sense. In our framework, all line handles are eventually converted to such positional constraints for interactive update during line handle manipulation.

Lines Drawing Line drawing algorithms make use of only few binary lines to convey the shape of (potentially complex) 3D models faithfully [CSD * 09] (see Fig. 1). We make use of those lines to generate line control handles for freeform deformation. In particular, our system allows to use any combination of Occluding Contours (OC), which are the isolines corresponding to null dot product between view direction and surface normal; Suggestive Contours [DFRS03] (SC), which correspond to OC visible from nearby point of view; Apparent Ridges [JDA07] (AR) which model surface curva-ture maxima once projected in the image plane; and Ridges and Valleys [START_REF] Interrante | Enhancing transparent skin surfaces with ridge and valley lines[END_REF] (RV) which correspond to curvature maxima and minima. Whith the exception of RV, all these lines are view-dependent, which means that the deformation control structure (i.e., line handle set) will adapt (smoothly) when moving the camera (see Fig. 1).

Line-based Deformation Control

Overview. Our modeling pipeline builds upon feature lines extraction to generate plausible handles automatically and can be summarized in a 5-stages loop (Fig. 2):

1. starting from a triangle surface mesh, a set of lines is extracted according to the current point of view; 2. lines are regularized to reduce their tangent variation; 3. a ROI is computed by diffusion on the surface, starting from the line selected by the user; 4. the ROI can be interactively adjusted to fit the target deformation scale; 5. the user manipulates the so-defined line handle and the surface is deformed accordingly using LRI.

From Feature Lines to Line Handles. At every frame, our system extracts feature lines stemming from line drawing algorithms (e.g., SC, AR, RV). Depending on the mesh structure and the view point, feature lines can be highly irregular, made of many disconnected components and exhibit a strong tangent variation (e.g., contour lines are not contained in front parallel planes). This often leads to a counterintuitive behavior of the ROI generation and subsequent deformation influence. Therefore, we start by applying an onsurface regularization scheme to feature lines. First, we sort feature lines by feature type (e.g, SC, AR). Second, we progressively aggregate small components by inspecting the 1neighborhood of each line extremity and connecting neigh- Occluding and Suggestive Contours.

Input
On-Surface Regularization.

Screen view Screen view

Actual lines in 3D Actual lines in 3D

Figure 3: On-Surface Line Regularization converts irregular and quaking feature lines to plausible handles. Three iterations were done in this example.

boring lines on a per-type basis. Components with a small screen-space projection are removed after this step according to a user-defined threshold (typically around 10 pixels in our experiments). Third, we regularize the resulting lines.

As we have to express the line regularization in the surface subspace -high frequencies removal should not make lines crossing or leaving the surface -we interleave a weigthed least squares filtering restricted to the line samples with a surface projection in an iterative procedure. More formally, let {p i } be the discrete set of points forming a feature line L on the input surface S. L is usually not a subset of the vertices of S. We define a regularized line L ′ = {p ′ i } by computing

p ′ i = Π k (p i ) = (Π S • F L ) k (p i ) with Π S (x)
the surface projection of x onto the closest location on S and F L (x) a weighted combination of geodesic neighbors of x w.r.t. L:

F L (x) = ∑ p i ∈Nx ω(||x -p i ||)p i ∑ i ω(||x -p i ||)
with Nx the geodesic neighbors of x in L. In practice we approximate this set by collecting Nx through a floodfilling process performed on L, starting at x and restricted to an euclidean ball of radius ε, the user-defined regularization scale (typically 5% of the bounding box diagonal). We use a compactly supported quartic polynomial weighting kernel [START_REF] Wendland | Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree[END_REF] behaving similarly to a gaussian kernel for a smaller computational footprint:

ω(t) = (1 -t ε ) 4 ( 4t ε + 1) if 0 ≤ t ≤ ε 0 if t > ε ,
This interleaved procedure is iterated k times or stopped when stabilizing. The algorithm is fast enough so that the user can adjust ε interactively. Figure 3 shows the result of this regularization procedure on a typical case.

ROI Definition. We recall that the ROI is composed of a handle H (Fig. 2, red), which strictly undergoes the usercontrolled rigid motion, and an influence area I (Fig. 2, blue) with geometry solved using LRI. As the handle defines positional constraints in the LRI system, we gather its ele-ments by collecting surface vertices located within a small neighborhood (within a ball of radius of 2% of the bounding box diagonal) around the selected (and regularized) line and apply the user's rigid transformation to them. Optionnaly, the user can apply non-rigid transformations using a linear blending decaying from full motion at the exact pointed location (e.g. mouse pointer) to null motion at the extremities of the line handle.

The influence area is also made of surface vertices and is gathered using a diffusion process starting from H. Let {v i } be the vertex set of S. As we want to grow uniformly I around H, we define the (discrete) distance function D H to be fast enough for interactive control:

D H (x) = 0 if x ∈ H argmin vi∈Nx (D H (v i )) + x -v i 2 )
otherwise with Nx the 1-neighborhood of vertex x. Thus, we collect I = {v i | D(v i ) ≤ δ I } using a Dijkstra flood-filling, starting from H. This diffusion process is bounded by δ I , the supervised size of I (30% of the bounding box diagonal in our experiments). The user can control independently the size of H and I to adjust the scale of the deformed region: H is controlled by bounding the distance to mouse pointer when gathering surface vertices composing it, while I is ruled by setting δ I . We provide an intuitive interface allowing to control this 2-dimensional bound using 2D mouse motions on the screen, with x-component sizing H and y-component sizing I. Last, at each user motion, the new positions of H are updated in the LRI system and I is solved.

Implementation and Results

We implemented our system in C++ using ings for the different steps of our line handle generation procedure, which clearly shows that our approach is fast enough for interactive editing on various models. In Fig. 5, we show simple examples of deformations which would have been tedious if the ROI had to be designed manually. Fig. 6 shows more complex examples, where different kinds of lines help modeling different structures on the surface. All of these deformations where made in one or less that one minute and from a single view point, except for the Goro that took four minutes and required a tens of view point.

Conclusion

We have introduced a new automatic handle generation technique exploiting the faithful salient features captured by Line Drawing algorithms to define a plausible interface for surface-based shape deformation techniques. We proposed efficient strategies to regularize the so-defined line handles and define a proper ROI dynamically, both processes being controlled by the user. As a result, the system allows to quickly edit complex shapes (e.g., piecewise smooth, arbitrary topology, boundaries) without resorting to a tedious manual selection step. Still, as our approach focuses on modifying existing surface features rather than inserting new ones, manual handle design remains available when needed. Figure 6: Our approach allows to deform complex models in few minutes. Left: using SC lines to deform the Goro.

Right: using RV lines to deform Jane.

Figure 1 :

 1 Figure 1: We build upon state-of-the-art line drawing methods and their ability to capture important view-dependent visual structures to construct FFD handles automatically.

Figure 2 :

 2 Figure 2: Overview: the user controls the deformation interactively in a select-and-grab loop. The handle's thickness is enhanced for readability.

Figure 5 :

 5 Figure 5: Using expressive lines as deformation control structures makes trivial editing actions that would be difficult with manual point-based or brush-based ROI selection.

Table 1 :

 1 OpenGL for rendering, RTSC [rts] for line extraction and CholMod [cho] for solving LRI deformation systems. In Table 1, we report tim-Timing for the different steps of our approach ( Intel Core2Duo at 2.2GHz with 4Gb of main memory).

	ROI Control
	Selection
	Figure 4: ROI control: adaptating the default ROI size
	(middle) enables a large panel of various deformations from
	a single handle. The handle's thickness is enhanced for read-
	ability.

c The Eurographics Association 2012.

c The Eurographics Association 2012.


Modeling Quality knowledge to design log sorting system by X rays tomography

Keywords: Quality control, X rays scanner, Knowledge modeling, NIAM/ORM

The aim of this paper is to highlight the advantage of using human (expert) knowledge and know-how to design an automated quality control system. This study is based on an industrial case of a French sawmill. The modeling deals with qualitative analyses of round wood and sawn timber in order to tune X rays scanner system. NIAM/ORM method is used to model quality expert knowledge. It enables to establish existing or missing links in one quality or between different levels of quality. This model will improve the optimization of one or several decision methods based on experts' knowledge of the company.

INTRODUCTION

Sawyer trade evolved since its creation. Sawyer tries to have a good material promotion in yield term and in qualitative term as well. In order to do that, sawmill is supplied with long stem (tree without branches) that it valorizes in short log, its selves sawed in final products responding to quality and dimensional requirements of the customers. Trade evolution affects the methods of forest management moreover. These methods can differ in function of the country uses, the historical planting of its territory, the directions chose by all the chain after major events (crisis, storms ...). In France, methods of forest management make supply heterogeneous in diameters, lengths, species, and even intrinsically to the wood. Effectively, wood is a living heterogeneous material because of its (1) gene pool (from specie to another), (2) its geographical localization (coordinates of the planting), (3) its stational situation (ground and sunshine of the tree) and (4) the area of the considered tree (butt end log to top end log). External events add to this natural heterogeneity. Insect attacks, storms, pruning, fungus attacks or animal's damage affect tree behavior, its structure and therefore, its quality. French sawmill uses this supply at the best, in order to take care and promote all parts of the stem. Input of French sawmills production process is a heterogeneous material intrinsically and a heterogeneous supply. In process output, sawmills are confronted with customers who want to have a homogeneous quality in product-to-product and parcel-to-parcel regardless where the wood comes from (except for certification aspect). Sawmill implements quality sort out on different levels of the production chain to surpass these constraints. The chain of the studied sawmill is represented by the block schema Fig. 1 in which "Log yard" block is detailed. The study presented in this paper is exclusively restricted to the qualitative analysis to optimize the stem in log. However, such analysis begins with getting pertinent information that enables to define wood characteristics: the paper includes definition of data given by the acquisition module. In addition, qualitative analysis evaluating the best sawing pattern is the sawmills main work (and secret) and it is the first factor of theirs customer Due to this factor, the paper will describe industrial production means only like a state of art and the results will be shown partially in the following section. Continuous improvements of the sawmill lead to create an automated log yard. In addition to the mechanizations which enable a right flexibility of supply, the log yard is equipped with a multi-sensors vision system that adds an external analysis and also an internal analysis of the stem according to the production chain speed.

The purpose of the study is to present the expended means to exploit data given by vision system. Our objective is to integrated knowledge in the decision system, as done in [START_REF] Bombardier | Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system[END_REF]]. The decision system becomes a useful support for the production operator who takes the final decision. The first step is to establish a model of "Wood quality". This quality model will enable to define quality criteria and be able to manipulate them in a "Quality" decision system. Originality of this study is that model provides from a downward approach (from operators or managers) (compare to the usual upward approach from scanning data). The purpose is to minimize the set-up time of the system. This model will be complete after, once the scanner set up on site.

For do it so, section 2 shows the studied system as a state of art, section 3 develops used analysis method to modeling and section 4 is focused on the first partial results. The conclusion explains our next works to integrate the knowledge into the (semi)-automated system.

DATA ACQUISITION SYSTEM

For more than twenty years, wood industries have used noncontact vision system in their production process. System performances help human decisions and sometimes enable to replace him. They give a better reliability, repeatability, accuracy or/and production rate [START_REF] Buehlmann | Impact of human error on lumber yield in rough mills[END_REF]. In another hand, those systems make it possible to access to information that naked eyes cannot detect. Microscopic and internal views of the material are possible by non-destructive and fast means. Sensors are mainly linked to surface analysis in furniture industries, cabinet making, laminated wood (for the color) and structural analysis in building industries too.

For example, when camera is combined to laser system, it is able to measure wood texture using reflectance. Vision systems using such sensors have good performances regarding surface analysis, as well as production rate as recognition rate. On sawing product, it is possible to accurately detect, localize and measure wood defects such as black knots, sound knots, resin pockets, compression wood, colored wood, blue stain or wanes [START_REF] Deng | Automatic sapstain detection in processed timber[END_REF][START_REF] Bombardier | Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system[END_REF]. Results obtained on surface analysis with camera are equal to ones obtained with X rays scanner. In addition to this, for this type of analysis, cameras make it possible to visualize color that no other sensor can measure. These properties are used in color sort out applications [START_REF] Faria | A computer vision system for color grading wood boards using fuzzy logic[END_REF][START_REF] Bombardier | A fuzzy sensor for color matching vision system[END_REF].

X rays scanners can also achieve aspect analysis but they are mainly used for structural analysis. X rays penetration enables to visualize internal wood defects. The accuracy of industrial sensor, at 2 or 3 meter/second production rate, did not enable to localize and to measure accurately knots or others defects. However, studies about algorithms show that detection, localization and quantification of knots in a short log are possible and reliable with a sufficient accuracy [START_REF] Longuetaud | Automatic detection of pith on CT images of spruce logs[END_REF][START_REF] Longuetaud | Automatic detection of annual growth units on Picea abies logs using optical and X-ray techniques[END_REF]Entacher K., 2007a[START_REF] Entacher | Pith detection on CT-cross-section images of logs : an experimental comparison[END_REF][START_REF] Oh | Quantification of knots in dimension lumber using a single-pass X-ray radiation[END_REF]. Recent progress done in X rays scanners oriented scientific research through image processing. Effectively, scanners now offer enough accuracy to defect recognition but some of them are not industrially viable because the acquisition speeds are still too slow.

Structural analysis finds most of its application in images reconstruction in two or three dimensions which enables to obtain supplementary information: the shape. Shapes of defect have an essential role in their detection and their identification (labeling). Structural analysis aims to internal defects detection, which is possible with X rays or gamma rays scanners. X rays scanners appeared to be the most promising tool in the preliminary studies that is why most researches were conducted on that kind of scanner [START_REF] Oja | Automatic grading of Scots pine sawlogs using an industrial X-ray log scanner[END_REF][START_REF] Longuetaud | Automatic detection of annual growth units on Picea abies logs using optical and X-ray techniques[END_REF][START_REF] Longuetaud | Automatic detection of the heartwood/sapwood boundary within Norway spruce logs by means of CT images[END_REF]Entacher K., 2007aEntacher K., & 2007b]]. There are three main types of X rays scanner: 2D, 4D and CT (Computed Tomography). 2D and 4D are respectively composed by two X rays sources at 90° or four sources at 36°. Those scanners give two (for 2D) or four (for 4D) images in two dimensions to analysis. CT enables several projected views by rotation of the source or by rotation of the piece (until 720 images at the moment). Then, projected views are combined to reconstruct the item en tree dimensions. 2D and 4D scanners are the ones mainly used in wood industries because their processing times are shorter. Recently, CT scanners get into industries due to their improvements on reconstruction algorithms. Detection results depend mainly on detection algorithms because scanner accuracy is now sufficient. Once defects are detected, structural analysis could be improved by correlating wood defects to quality [START_REF] Wei | Identification of selected internal wood characteristics in computed tomography images of black spruce: a comparison study[END_REF] or wood strength [START_REF] Brännström | Predicting board strength by X-ray scanning of logs: The impact of differnet measurement concepts[END_REF].

Vision systems are able to give reliability information to following module [START_REF] Rojas | Identification of internal defect of sugar maple logs from CT images using supervised classification methods[END_REF]. In that way, the main industrial issue is not only to obtain information but also to define (1) which the pertinent information are regarding objectives and (2) how to use them in order to automate actions made by human beings and based on their experience. The answer of these two issues is given and integrated by the module of qualitative analysis (cf Fig. 1). This module must hold experience knowledge owned by the company, and needed to set up and integrate the vision system. Another difficulty consists in taking into account this knowledge and transcript it to the control system. This implies a strong work relationship between experts in several domains (quality, computer programming, optimization...). It is necessary that all actors of the study have the same understanding of the system to reach goals. Developing a model of wood quality knowledge at different levels of the production chain is the best way to define problems. It cans lead to a specific ontology creation to enable collaboration between actors implementing the system.

Achievement of different quality models of the company enables to (1) establish links between different levels of quality; (2) specify quality really used or planned in the future;

(3) define input information of qualitative analysis in the new system; (4) select an adapted computed decision system and (5) set up the decision system.

KNOWLEDGE MODELING

To capitalize sawyer's know-how, useful in the achievement of the control system, Natural language Information Analysis Method (NIAM) is used [START_REF] Blaise | Formalisation of normative knowledge for safe design[END_REF]. It enables to model beginning with a knowledge express in natural language. Moreover, the ORM (Object Role Modeling) formalism is used to write the model. After presenting the main knowledge modeling principles and methods, we will describe NIAM in this section.

Modeling principles

Main difficulties in model conception are the acquisition of information (more in the tacit domain) and the fact that information is owned by several experts (merge overall to reach one final objective). Achievement of a system using knowledge follows three processes that it makes possible to pass from a tacit informal knowledge to an explicit formal knowledge usable by all the actors. These three processes are:

(1) acquisition of knowledge;

(2) formalization of knowledge and (3) mining of modeling knowledge (cf. Fig 2). This paper mainly concerns the couple acquisition/formalization of knowledge but expresses possible ways for knowledge mining in future works (Cf. Discussion and conclusion).

Acquisition of knowledge

Operator or manager's knowledge is implicit (tacit) for himself. It means that when he expresses it, the terms he uses can be ambiguous or refer to a knowledge non-known by everyone. The transmission of information in industrial domain is mainly done in natural language. This language is quasi-perfect between humans [START_REF] Andrade | Definition of a problem-sensitive conceptual modelling language: foundations and application to software engineering[END_REF] but it shows limitations in some situations. Several sources could distort expressed knowledge: (1) constitution itself of the language which can be ambiguous in function of the complexity or the specificity of expertise area; or (2) the use of indirect means to express knowledge (synonyms, neighbor concepts, intermediate level of abstraction) which lead to an approximation of the knowledge.

Two approaches are usually distinguished to acquire knowledge: manual approaches (or knowledge engineering approaches) and automated approaches based on software engineering. Knowledge engineering approaches imply the use of interview techniques based on psychology works. Among these methods, we can count: interviews, verbal protocol analysis done by live-record of expert during problem resolution, direct observation, the use of questionnaire, and brainwriting. Automated approaches aim to highlight a significant knowledge based on a large data base.

In this study, knowledge engineering methods are mainly used. Knowledge engineer interviewed production operators and managers and he followed training session about sales, wood quality, wood supply and forestry as well. Knowledge becomes explicit informal.

Formalization of knowledge

This step purpose is to transform an explicit informal knowledge into an explicit formal knowledge. Three approaches can be used to extract knowledge in a structured way: upward approach, downward approach and mix approach. Upward approach implies that knowledge engineer collects as many information as possible without leading or influencing the interviewed expert. Model is built by successive addition of information (iterative construction). Whereas, downward approach leads the engineer directly to the final model definition screening-off the knowledge and lead efficient acquisition process; knowledge engineer must be skilled in expertise system. Mix approach combines upward approach first and downward approach then in the same formalization. Mix approach is used in this study.

Model construction is based on acquisition and formalization of knowledge. Related to the concerned area, conceptual modeling uses different forms of natural language. Structural document analysis is mostly used to recognize entities and relations between them. Another form uses linguistic area to build a conceptual model and makes sure that model content is led by specifications in natural language. Finally, natural language is the best mean to communicate because, in that case, it is the only comprehensible language by all project actors. Natural language is an essential factor of this study.

Several formalization methods using natural language are explained in literature. We cite Natural language Information Analysis Method (NIAM) known as NIAM/ORM due to its derivative formalism ORM (Object Role Modeling) [START_REF] Blaise | Formalisation of normative knowledge for safe design[END_REF], Conceptual Graphs (CG) [Dibie-Barthélemy J., 2006], Object Conceptual Prototyping Language (OCPL) [START_REF] James | The development of OCPL, object conceptual prototyping language[END_REF]. Moreover, softwares are developed to acquire information from a domain and/or build an associated model. These tools use natural language processing elements which analyze texts and extract pertinent information to construct searched ontology. Textual analysis is based on syntax relations between terms.

Each mentioned method explains its own ontology building an approach of the domain. However, none of them gives information about the model validity. Wagner JC [START_REF] Wagner | Natural language generation of surgical procedures[END_REF]] created modules able to translate conceptual models into natural language to (1) validate knowledge models with formalism close to the natural language and to (2) re-use modeling knowledge without being an expert of the used formalism. It's in the same way that NIAM enables an easy translation into natural language, comprehensible by layman and wherein (manual) generation has to be done by knowledge engineer.

In this study, NIAM/ORM is used: others methods are either (1) too specific of an area and need to be modified by external tools to be compatible, or (2) use formalism of which legibility is not easy and its utilization is not natural (CG and OPCL approaches), or (3) are associated with a transcription tool which is too complex and not very efficient. The simplicity of transcription tool is the main asset of NIAM/ORM, moreover, it is easy to use and to implement.

NIAM/ORM modeling method

The main purpose of this method is to analyze knowledge from a specific area (named Speech Universe or Interest Universe). In order to make this, this method uses a descriptive language from the real world in term of objects playing roles. Asset of ORM formalism, compared to the most known UML (United Modeling Language), is its ability to express information with simple relations; it is the real leitmotiv of NIAM. The method distinguishes Interest Universe on two systems: the object system and the abstraction system (see Fig. 3).

Modeling method reasoning is based on three axioms ensuring the quality of the obtained model: (1) semantic Fig. 2. Process to construct system using knowledge matching between facts in natural language and several elementary sentences;

(2) elementary sentence expressions highlight "idea" and "denomination bridge"; and (3) determination of all possible conditions and all enabled transitions in the data base.

Elementary sentence is a sentence which cannot be decomposed in shorter sentence without loss of semantic. These decompositions enable to minimize ambiguities, minimize shortcut inherent to natural language use. Ideas and denomination bridges respectively represent links between two abstract objects and, links between abstract objects and the objects they represent in the reality. Ideas carry information contained (and needed) in the conceptual model. Denomination bridges do not carry information: they only help to represent real part of Interest Universe in the final data base. Finally, constraint additions on objects composing an idea must be done to be sure about the coherence between modeling knowledge and observed facts.

In practice, NIAM/ORM is deployed in four steps: (1) acquisition of knowledge;

(2) modeling of knowledge; (3) model validation and (4) formalized knowledge validation. This paper deals with knowledge acquisition and modeling. At the moment, the validation is done by the production experts (sawmill operators and managers). After, a final validation will be done by data analysis provided by the vision system. Moreover, due to confidentiality terms, following presented results are under-group or part of global model.

"QUALITY" MODELS AND ANALYSES

This section describes NIAM/ORM application to our study case. It aims to give "Sawn timber quality" and "Log quality" knowledge models which will be mine during set up stage of the vision system.

Acquisition of expert knowledge in wood grading

Mix approach is used in this extraction of knowledge. Upward approach is done by a training of the knowledge engineer in expertise area. He integrated production shift which realize wood grading. This training is made in all workstations of the factory where a quality decision is taken: on round wood and on sawn wood. Moreover, theoretical training is done by others company department (commercial, strategic, purchase) and enhanced practical training. Those enable to understand instructions given to production department. Knowledge engineer is able to specify wood quality according to his direct observations and his own comprehension of the different training. Downward phase consists in creating a questionnaire given to experts and completed during their interview.

Engineer obtains two different expertises: one on round wood quality and the other one on sawn timber quality. The one on round wood quality aims to express a "log" quality function of physical characteristics on the shape of the stem. Sawn wood quality expresses a "sawn timber" quality function of characteristics visible on the four sides of boards. Engineer extracts whole characteristics from these expertises to define both qualities. These characteristics will be used as decision criteria to final quality sort out.

These two qualities are linked one to the other by geometric or anatomic relations. For example, criterion "visible diameter of dead knot" on the sawn timber is geometrically linked to criterion "diameter of dead knot" of the round timber. This relation takes into account knot form, sawn position in the log, etc. To explain anatomic relation, and due to confidentiality reason, we will name a wood characteristic "Characteristic Y" because it's extracted from the company know-how. So, as an example, criterion « Presence of embedded bark » evaluated on sawn timber is anatomically linked to criterion "Presence of Characteristic Y" in most cases because this characteristic is often (but not always) associated with embedded bark. Quality modeling highlights existing links inside a quality, existing links between two different qualities, and above all, missing links between qualities. It's actually this number of missing links that the new log yard tends to reduce.

Knowledge modeling of wood experts

Log quality experts describe each wood characteristic. They describe for instance a dead knot as follow: "A dead knot is like a dark and round stain in wood surface in most of the case. Otherwise, a dead knot can be light but ringed The engineer obtains models for sawn timber quality and for log quality. Parts of models are given Fig. 6. Comparison of both models highlights links between qualities. For example, models given in Fig. 6 highlight direct link between both measures of dead knot diameter (point 1), indirect link between embedded bark measure on sawn timber and characteristic Y measure on log (point 2) and, lack of link between embedded bark of sawn timber and the one of log (point 3). That's how models support engineer work which aim to reduce number of missing links and also improve coherence of existing links. Links highlighting isn't automated. Today, only experts can judge link coherence. In this study, major part of criteria (needed to definition of sawn timber quality) could be linked to one or several criteria of the log quality. Missing links concern material deteriorations that are too small to be visible by the vision system or with no effect on the measured material properties. In addition, created links are often indirect. They are made either by geometrical correlation or by statistical correlation based on previous scientific studies or on the staff experience.

Comparison results of both qualities enable to highlight a new log quality which will be implemented on the log yard. This log quality will be a combination of more than 40 criteria. In addition, beyond the quality definition, modeling highlights characteristics that stem optimization and valorization module (see Fig. 1) have to own (These results will be presented in a next study).

Syntax analyses of definitions given by experts are also expressed in models. A hierarchical organization of criteria appears which highlights structure of the expert's mind. For example, on a dead knot in the stem: "Diameter has to be less than 25 mm to a "Q2" quality if the stem diameter is higher than 35 cm on this stem part". Denomination bridge lead, step by step, to the final answer.

DISCUSSION AND CONCLUSION

Knowledge modeling in an industrial area enables to improve system comprehension of each expert, and also enables a collaborative work between them. Integration of a new expert with a new knowledge or a new point of view at any time is made easier. Beyond the better staff synergy, creating models with NIAM/ORM method enables, a posteriori, a validation of models by all the experts, even the ones who are not familiarize with ORM formalism. In this study, the validation of the model is done by quality experts. The first model is based on human knowledge and its analyses emphasize existing links and missing links inside a quality and between levels of qualities. ORM is currently used to formalize knowledge. That can be considerate as creation of an expert ontology. Such ontology can improve dialogue and comprehension between actors easily. Analyses on the final results will lead to the selection of pertinent criteria, pertinent thresholds and an optimal hierarchical structure of the qualitative decision system. This work enables to capitalize and homogenize quality knowledge and know-how of the company.

At the moment, NIAM/ORM models emphasize criteria needed to define quality and highlight also mind structure of experts who are the owners of knowledge. These works are a strong basis to launch the new system when it will be provided.

Once the system on site, we will be able to compare the decision system (and the knowledge model) to the reality. Data analysis should challenge the decision system and the knowledge model.

  Form type Round defines one and only one Wood characteristic or one or several Wood characteristics?"_ Answer: One or several. Fig.5shows formalization of the answers.

	Abstraction system	3		Conceptual schema
	2
	Object system
		1	Reality
	1-Observation (knowledge acquisition)
	2-Abstraction: objects, types of facts,...
	3-Formalization
	Fig. 3. Object and abstraction system following
	NIAM/ORM method.
	is defined by one and only one Form or by one or several
	Forms?"_ Answer: One and only one. Question (3) on
	totality of the object Form: "Is each Form defined a Wood
	characteristic?"_ Answer: No. Question (4) on uniqueness of
	Form: "A
					These kinds of definitions use ambiguous terms, so
	Log	can be	is	Dead knot	knowledge engineer must use this own knowledge to interpret what experts told him. Following NIAM/ORM {'mean diameter'} Diameter measure 1 evaluate is evaluated
	Wood charact.	can be	is	Charact. Y	method (and the engineer using it), translation in elementary {'Measured Y'} sentences can be: "Dead knot is contrasted to main wood color. Dead knot is round. Dead knot is usually dark. Dead measure Measure Y 2 evaluate is evaluated
					knot can be light. If dead knot is light, it is ringed by bark
				is associate	ring."
				can be associate	Then, these sentences are translated in their "deeper form". With the previous example:
					-Dead knot is round => the wood characteristic "Dead
		can be	is	Embedded bark	knot" have a form "Round" 3 ?
					Elementary sentences, illustrating real facts, are translated
				can be associate	into ORM formalism. For example, following sentences are
				is associate	in Fig. 4: Dead knot is round. Dead knot is usually dark. Dead knot can be light. {'hurt, fork'}
		can be	is	Growth perturb	Using this, knowledge engineer extracts only ideas to obtain
	Sawn timber	can be	is	Dead knot	basis of generic model. He then can interrogate him-self or {'visible diameter'} asks experts to establish constraints between objects of this model. For example, Question (1) on totality of the object is evaluated measure evaluate Diameter 1
	Wood charact.	can be	is	Embedded bark	Wood characteristic: "Is each Wood characteristic defined by Wood characteristic: "A Wood characteristic type Dead knot is evaluated 2 and 3 measure evaluate Presence a Form?"_ Answer: Yes. Question (2) on uniqueness of {'side presence}
	Fig. 6. Partial generic models of sawn timber quality and log quality

[surround] by a ring [of bark]. [...]".

  It's obvious that expert judges successively (1) a precise part in the stem wherein (2) he evaluates stem diameter to finally (3) evaluate knot diameter regarding stem diameter. Models enable to organize expert decision method into a hierarchical way. However, there is no warranty about effectiveness and efficiency of this structure. In this study, hierarchical structure is established manually but syntax analysis software cans generate it automatically. Manual structure establishment takes lot of time and needs a high focusing of the knowledge engineer. He often faces incoherencies or contradictions that he can't correct by himself. He must refer again to experts who clarify, correct and improve information to surpass this situation. Results show a hierarchical decision of the log quality on 8 levels. Levels directly lead to sought quality or reduced decision range by eliminating impossible quality and

					{'dead knot'}	(2)	(3)	{'round'}
	Form Idea {'dead knot'}	can be is defined	is defines	Round {'round'} value} Form {form	Wood charact {type} Fig. 5. Formalization of uniqueness and totality is defined (1) (4) define constraints	Form {form value}
	Wood
	charact.			{'dark' ;'light'}
	{type}
		is defined	defines	Color {name color}
	Fig. 4. Formalization with ORM