Manja Görner

Thomas Göschel

Stephan Kassel

Sabrina Sander
email: sabrina.sander@fh-zwickau.de

Thomas Klein
email: thomas.klein@fh-zwickau.de

Thomas Goerner

Stephan Goeschel

Kassel

An Ontology for Interoperability: Modeling of Composite Services in the Smart Home Environment

Keywords: composite services, interoperability, ontology, smart home

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In the field of smart home, it is a general goal to bring more intelligence to the automation systems. [START_REF] Bregman | Smart Home Intelligence -The eHome that Learns[END_REF] Moreover, the efficiency in using energy should be increased. One approach is using photovoltaic systems (PV systems). With these systems the consumer can generate his or her own power. The generated electricity is fed into the own and surpluses in the public network. Currently, the DC voltage (direct current) generated by a PV system is alternately directed into AC voltage (alternating current). But many appliances in households based on DC voltage. So AC voltage is rectified back in the device's power supply. These inverter and rectifiers create significant energy losses. In this respect, the direct use of DC voltage in the power grid is interesting.

In the project EGNIAS at the University of Applied Sciences Zwickau the potentials of using DC voltage in the smart home (SH) environment were examined.

Therefore appliances, which are used in SHs and based on DC voltage, have been identified. On this device landscape services have been defined. These services are "composite services", which can directly influence physical environmental parameters. The focus in this paper is, how these services can be represented in an ontology and thus made available to other processes. For this, firstly the concept of a "composite service" is presented. Then the existing research contributions and ontology approaches are shown. From the results of this inspection, the need to conceive an own ontology for the "composite services" is demonstrated. The developed concept will be presented and applied for a specific service.

Composite Services

First, it is necessary to identify appliances which can be operated with DC voltage. It turns out that these can be found mainly in the field of low voltage. According to the Energy Information Agency (EIA), the fastest growing portion of residential electricity use is consumer electronics and small appliances. [2] Therefore, the selection of the units has focused on this sector. Exceptions are the selected lamps. LED lamps are also suitable for the project, which is why they were included. So among other things the device landscape contains a phone, a tablet PC, a radio and a variety of LED lamps. On this basis the "composite services" were defined. The aim is that the room can be set to different use alternatives. Special attention must be set to the subjectivity of human perception assets. For example, a person's perception of the same light intensity is different in various situations. This contrasts with the system's objectivity that is controlled with absolute values. Therefore, the user needs must be analyzed, interpreted and integrated accordingly in the services. For example, the illumination of room areas can be differentiated. In this regard manual changes of the user are stored and used as a standard for the next application. Thus, the services are customizable.

A composite service is defined as a process to influence physical environmental parameters selectively to create a pleasant and activity appropriated room atmosphere. So with a composite service not a single device is controlled but a variety of devices is coordinated. The services are therefore not fine-grained, which represents a new approach in building automation.

The services will now be displayed in such a way that it is compatible with existing approaches. The design of the defined services shall be formally mapped. This promotes compatibility in such a way, that the composite services can also be integrated into other room control systems.

Related Literature

In the topic smart home there exist a number of research activities, which are focused on services. 1 Moji Wei et al. [START_REF] Wei | Ontology-Based Home Service Model[END_REF] present an "Ontology Based Home Service Model". That is a model to retrieve and invoke services according to user's needs automatically. But firstly needs must be determined. The contribution mainly deals with how these needs can be determined. The services, which are retrieved, include the retrieval of a single device. However, the here defined composite services do not meet this fine-grained structure and thus they represent a continuation of the approach of Moji Wei et al.

Mobility and heterogeneity are characteristics of many devices in the SH. In order to manage these devices efficiently, the system CASSF (Context-Aware Service Scheduling Framework) [START_REF] Liang | A context-aware multimedia service scheduling framework in smart homes[END_REF] filters out and offers suitable services. This is done according to the task requirements (TR) and serves an improved user experience and content adaptation. Therefore the devices and their functions were mapped in an ontology, a context-sensitive service selection program was developed and a method for content adaptation was proposed. This shall achieve an enhanced user satisfaction. In CASSF functions are derived from the user's needs. This is the basis for the services. The analysis of the needs was based on Maslow's hierarchy of needs [START_REF] Maslow | A theory of human motivation[END_REF]. The needs are interpreted with the aim of providing an appropriate service. But in the here presented concept it is the point that the user tells the system his planned activities and expected that the room sets on it, for example, by an appropriate lighting scenario. Which scenario is to provide in a situation was indicated to the system within the customizing. So in the concept the analysis of needs is not done by the system. This distinguishes the CASSF approach from the shown one. However, considering that at the same time there could be several people in a room, which have different requirements, these requirements have to be reconciled and the related services have to be combined. For this service combination CASSF could provide a solution approach.

The approach presented by Yung-Wei Kao and Shyan-Ming Yuan [START_REF] Kao | User-configurable semantic home automation[END_REF] is based on the theory that a user in a smart home thinks semantically. His request to the system might be: "I want to turn off all the lights on the second floor." To illustrate this in an ontology USHA (User-configurable Semantic Home Automation System) and a selfdefined markup language SHPL (Semantic Home Process Language) were developed. SHPL is able to semantically link the information "all" or "none" and the concept of belonging [START_REF] Kao | User-configurable semantic home automation[END_REF]. Thus "I want to turn off all the lights on the second floor" can be implemented.

The presented approach goes on one step further in this relationship. The user does not need to selectively turn on or off the lights and appliances. Instead, he tells the system only, which is his planned activity and the room sets on it automatically. Therefore the devices are addressed differentiated. For example, all the lamps of a region are turned on, but only with a power of 25%, except lamp XY, which is turned on with full power. With our approach this central aim of context-aware systems is 1 As in the project EGNIAS energy efficiency plays a big role in the smart home environment.

For example in [START_REF] Nguyen | Energy intelligent buildings based on user activity: A survey[END_REF] an empirical study which deals with this issue and which is based on an analysis of user activities is presented.

achieved. Even the following circumstance is feasible: If the environment due to external influences is already so bright that the full power of lamp XY does no longer increase this brightness, the lamp does not need to be turned on or only with a lower power.

Basic Ontologies

The conception of the defined composite services shall be formally mapped. This is to promote compatibility in such a way that the design can also be integrated into other systems than the control room of the project. For such a mapping ontologies are very useful. There are a number of different ontologies. But all were situated within a particular context. In this chapter two ontologies are presented, whose contexts have a high degree of relationship to the described developed services in the smart home environment. The aim is to illustrate the ontology's contribution to map the presented composite services.

Using DogOnt [START_REF] Bonino | DogOnt -Ontology Modeling for Intelligent Domotic Environments[END_REF], modeling an intelligent, automated environment with the devices, their status, functions and messages, as well as the architecture is possible (Figure 1). It seems to be the most complete ontology for modeling building components. It includes a wide taxonomical organization of controllable devices (appliances, home plants and home gateways), including their functions (control, notification and query) and notifications. However, no description of hardware features of the devices is possible. This is important for example to determine the quality of the offered service. [START_REF] Grassi | Ontologies for Smart Homes and energy management: An implementation-driven survey[END_REF] In addition DogOnt is not able to map composite services, as here defined. Although it contains commands by which the command to individual devices can be mapped. But composite services include a variety of commands to a variety of devices. The ability to map this instruction bundle, which is furthermore derived from corresponding input factors, is missing in DogOnt.

Instead Owl-S [START_REF] Martin | OWL-S: Semantic Markup for Web Services[END_REF] is able to describe scenarios and services. It makes possible to discover, invoke, compose, and monitor Web resources to offer particular services and supports to do it with a high degree of automation. In OWL-S services with an input and an output are defined (Figure 2).

Figure 2 The process ontology of OWL-S 3

This approach is picked up by Davy Preuveneers and Yolande Berbers [START_REF] Preuveneers | Automated context-driven composition of pervasive services to alleviate non-functional concerns[END_REF]. They developed the ontology CODAMOS. They use this ontology in service-oriented computing context. But web services do not include the functionalities which comprise the here defined composite services. However, they provided a decisive approach to the development of an ontology which is able to map these composite services.

Research Contribution

The representation of something real in the meaning of ontologies includes a conceptual layer and identities layer. In the conceptual layer the general construct with corresponding relations is shown. The identities-layer is a parallel level to the conceptual layer. In the world of ontologies identities are the specific properties of the object. Therefore in the identities layer the real objects, based on the structure of the conceptual layer, can be mapped.

The following part shows how a non-fine-grained composite service can be mapped in ontologies. Therefore a concrete example of a composite service is presented. Subsequently, the conceptual layer is explained, therewith such a non-finegrained service can be mapped. Finally, the presented service example is mapped on the identity layer.

In Figure 3 there is to see a schematic representation of the model room, which is constructed by us. The lamps are controlled directly and the devices by means of the sockets. Based on this devices landscape the services have been defined. As an example the welcome_service shall be mapped. The welcome_service is the basic service of the control. The aim of this service is to illuminate brightly the entire room. Therefore the dimmable lamps over the dining table (B2) and over the lounch table (B3) are switched on with full power. The floor lamp next to the couch is also turned on. This is connected to a socket (SD3), which must be activated.

Because DogOnt is basically able to represent the devices and components in a SH, it makes sense to extend this ontology. To incorporate the notion of a process, the process design of OWL-S can be taken up. For the input and output of the services DogOnt provides its own relevant states and commands.

The conceptual layer:

A service is a subclass of a process. A process has an input and an output. The input consists of the room_usage_type, the light_intensity_state and a brightness_reference_state. The room_usage_type and brightness_reference_state are a discrete_state. The various room-usage-types in the EGNIAS-project are "welcome", "dining", "working", "reading", "music" and "tv". These are variables of the room control. With them the planned activities of the user are stored in the system. The brightness_reference_state is a parameter which is required for room control. A brightness_reference_value is associated with it. The light_intensity_state is already defined in DogOnt.

A process generates commands. Thus commands are the output of a process. Commands are also defined in DogOnt. A subclass is added here: the step_up/down_command. This compares a controllable_building_thing's state_value with a reference_value and adjusts the state_value to the reference_value.

The commands affect an object of controllable_building_thing, which is represented by the property for.

Figure 4 shows this construction in the conceptual layer. and the lounch area (brightness_reference_state_lounch_area) with the value 100 (percent) as well as the light_intensity_state for these areas (light_intensity_state_lounch_area, light_intensity_state_lounch_area) as input. The values of these last two states are provided by brightness sensors. The welcome_service's output contains of an on_command for the socket (on_command_SD3), step_up/down_commands for the dimmer lamps (stept_up/down_command_B2, step_up/down_command_B3) as well as off_commands for all other sockets and lamps. Figure 5 shows the identity layer for the welcome_service.

Conclusion and Further Work

The starting points of this paper are two general goals: more intelligence and more energy efficiency in the Smart Home. In the project EGNIAS these aspects were picked up and linked. The result of the project activity is a model room. This model room is equipped with appliances whose functionality is based on DC voltage. A power grid was implemented, which supplies the appliances directly with the appropriate DC voltage. On this device landscape composite services were set up. A composite service is defined as a process to influence physical environmental parameters. By choosing a room usage type (dining, reading, working …) the room shall create a pleasant room atmosphere that is appropriate to the user's planned activity. So composite services are not fine-grained, because a variety of devices is coordinated to each other.

To make the whole system compatible to other systems the composite services were mapped in an ontology. It was realized by picking parts of DogOnt and OWL-S. The reuse of existing ontologies reflects the desire for compatibility. In DogOnt the concept of a smart home is mapped extensively. OWL-S provides the design of a process, but relates to computing services. Therefore, the concept of a service in OWL-S has to be adapted to the concept of a composite service. It also was combined with elements of DogOnt. So these two ontologies have been linked and a composite service can be mapped.

Because the device landscape's control is not yet flexibly variable, the current implementation of services can be classified as static. Each device has to be introduced manually to the system. This still does not meet the dynamism with which a user is acting in his apartment and within he should be supported. Further research activities will therefore deal with the problem, how new devices can be dynamically inserted into the system. A solution could be a communication via UPnP. The devices' required information could be stored in the cloud and loaded from there as needed in the system. Furthermore, a possibility to accordingly extending of existing services or to defining new services has to be found.

Figure 1

 1 Figure 1 Overview of the DogOnt ontology 2

Figure 3

 3 Figure 3The model room

Figure 4

 4 Figure 4 The ontology's conceptual layer

Figure 5

 5 Figure 5 The ontology's identity layer

source: based on[START_REF] Liang | A context-aware multimedia service scheduling framework in smart homes[END_REF] p. 794.

source: http://www.w3.org/Submission/2004/SUBM-OWL-S-20041122/


Charles Crick

Eng K Chew
email: eng.chew@uts.edu.au

Introducing a Socio-Technical Perspective on Business Processes into Enterprise Interoperability Frameworks

Keywords: Socio-technical systems, business process ontology, technology affordance, enterprise interoperability, dynamic capabilities

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Generally, an Enterprise Interoperability Framework (EIF) is a way of structuring knowledge in the enterprise interoperability (EI) domain, such that we may reason about problems such as barriers to interoperability and understand possible solutions. As pointed out by Guédria et al. [START_REF] Guédria | Maturity model for enterprise interoperability[END_REF], in their review of the state of research in this area, there are several EIFs in existence, well known examples including the ATHENA (Advanced Technologies for interoperability Heterogeneous Enterprise Networks and Applications) Interoperability Framework [START_REF] Ruggaber | ATHENA -Advanced Technologies for Interoperability of Heterogeneous Enterprise Networks and their Applications[END_REF]; the European Interoperability Framework [START_REF]EIF: European Interoperability Framework, White Paper[END_REF]; and the FEI [START_REF] Chen | Framework for enterprise interoperability[END_REF][START_REF] Chen | Enterprise Interoperability[END_REF], the latter is also a published ISO standard. These frameworks have their foundations in the discipline of Enterprise Architecture [START_REF] Chen | Architectures for enterprise integration and interoperability: Past, present and future[END_REF], where the enterprise is modeled across a number of domains (such as business, data, technology), at a number of levels of abstraction (e.g. conceptual to physical) and generally in terms of deterministic artefacts such as business processes, data entities, applications and so forth. We use the term deterministic in the sense that the artefacts are well defined objects that can be designed, analysed and modeled etc. In this paradigm, interoperability is characterised in terms of relationships between these objects, for example, communicating IT applications or business processes that need to interoperate in order to deliver an outcome. This representation also facilitates fairly straightforward quantification of interoperability, such as with the i-Score method [START_REF] Ford | The Interoperability Score[END_REF], since the relationships and artefacts can be reduced to deterministic graph structures that can be analysed with mature algorithms.

In this paper we suggest the need for an additional ingredient in this EI picture, one that is bound up in the intrinsic non-determinacy of the human actors that take part in the activities of the enterprise, and which must therefore, we believe, be taken into account when we want to talk about behavioral aspects of EI. Naudet et al. [START_REF] Naudet | Towards a systemic formalisation of interoperability[END_REF] hint at this issue in their characterisation of organisational interoperability concerns, but in our view fall short of catering for the implicit dynamics involved.

Our response to this issue is to include a socio-technical dimension in the conceptualisation of business process, informing this view, in particular, with the theory of organisational routines [START_REF] Pentland | Organizational routines as a unit of analysis[END_REF] and technology affordances [START_REF] Zammuto | Information technology and the changing fabric of organization[END_REF][START_REF] Leonardi | When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies[END_REF], drawn from management sciences, both of which centre around the role of the human agency. We argue that these concepts introduce an adaptive element into the idea of interoperating processes such that a barrier in, for example, the technology layer may be simply "worked around" by improvisation. Our main point is that this occurs as part of business-as-usual, rather than through any intentional intervention. As argued by Schreyögg and Kliesch-Eberl [START_REF] Schreyogg | How dynamic can organizational capabilities be? Towards a dual-process model of capability dynamization[END_REF], this type of organisational learning places a requirement on the part of management to exercise a second-order activity that monitors business-as-usual to detect and make corrections. Such second-order activities, or dynamic capabilities [START_REF] Teece | Dynamic capabilities and strategic management[END_REF], are crucial mechanisms by which organisations maintain their alignment with the environment, or their evolutionary fitness [START_REF] Helfat | Dynamic capabilities: understanding strategic change in organizations[END_REF], which includes their ability to maintain interoperability.

The remainder of this paper is structured as follows. First we set out our conceptual model of business process and provide its theoretical underpinnings. We then go on to discuss how this impacts the EI ontological model [START_REF] Chen | Framework for enterprise interoperability[END_REF][START_REF] Naudet | Towards a systemic formalisation of interoperability[END_REF].

2

Conceptualising Business Process as a Socio-Technical Object

Ontological Perspectives

The business process (BP) is a familiar Information Systems (IS) concept. Weske's definition is a typical definition used in the IS literature: "A business process consists of a set of activities that are performed in coordination in an organisational and technical environment. These activities jointly realize a business goal. Each business process is enacted by a single organisation, but it may interact with business processes performed by other organisations." [15 p.6]. The idea of BP as deterministic, executable entities is central to this conceptualisation and has led to the rise of Business Process Management (BPM) and associated technologies as a popular IS discipline [START_REF] Van Der Aalst | Business process management: A survey[END_REF]. The conventional modeling and analysis of the interoperability of business processes also relies on this view. In some cases, of course, business processes are fully automated and operate without any human participation, and are in that sense deter-ministic. However, we argue, when there is human agency involved in the processes, this is an overly simplistic perspective, and any ontology that underpins a model of interoperability needs a way of teasing apart the deterministic and the nondeterministic elements.

Attempts have been made to admit other ontological perspectives into the business process concept, such as BP based on complex systems theory [e.g. 17, 18], for example. The importance of context when considering the instantiation of a BP has been recognised, both in terms of how it contributes to flexibility [START_REF] Rosemann | Contextualisation of business processes[END_REF] and how it can be modeled [START_REF] Saidani | Towards context aware business process modelling[END_REF]. There are several dimensions that provide context, such as, for example, the circumstances of the organisational environment providing the backdrop for the particular BP instance, or substitution of different participant roles when the process is actually executed. The BP literature is mostly silent, however, on the contextualisation attributable to the human participants, or other words, how the human participants construct the business process instance, in the act of practicing it, within a given situation.

The organisational routine concept [START_REF] Pentland | Organizational routines as a unit of analysis[END_REF], which has been developed in the management sciences largely outside of IS, provides a relevant insight into the role of human agency. This theory distinguishes a duality of "ostensive" and "performative" facetsthe former representing the idealised, codified representation of the routine and the latter the routine-in-use, or what actually happens in practice. The implication here is that the routine may be performed differently each time it is repeated even if the ostensive aspect remains the same. We argue the distinction between the ostensive and performative aspects also has relevance to how business process should be conceptualised. It suggests that there has been a missing ingredient in the traditional IS orthodoxy when it comes to business processes [e.g. [START_REF] Weske | Business Process Management -Concepts Languages Architectures[END_REF][START_REF] Van Der Aalst | Business process management: A survey[END_REF][START_REF] Hammer | Reengineering the Corporation: Manifesto for Business Revolution[END_REF]. That is, we cannot treat a business process merely as an artefact that can be deterministically executed. Instead we argue that the non-determinacy of human agency must be factored in: the processas-designed is different from the process-as-performed. Human factors such as motivation, skills, tacit knowledge and experience, intrinsically mean the process may not deliver what was "intended".

The Role of Technology

The idea of the performative routine is taken a step further into the socio-technical realm by the concept of the technology affordance [START_REF] Zammuto | Information technology and the changing fabric of organization[END_REF][START_REF] Leonardi | When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies[END_REF][START_REF] Majchrzak | Technology affordances and constraints[END_REF][START_REF] Yoo | Organizing for innovation in the digitized world[END_REF]. An affordance represents the perception of what can be done with an item of technology by a user with a particular goali.e. the affordance is the potentiality for action of a technology feature, not necessarily how the feature was designed. Thus the way technology is used (by a human user) in the business process is a function of the potentiality of the technology (for action) as perceived by the users, rather than just a set of pre-designed technology features.

Conceptual Model

In this section we explain our conceptualisation of business process that has been informed by the theoretical foundations discussed above. The purpose here is to understand how the human-centred factors give rise to intrinsically adaptive behavior whereby the business processes can evolve away from any a priori design, with ramifications for process-based enterprise interoperability.

We start with the intra-organisational view depicted in Fig. 1. This model situates the business process concept within the enterprise using the idea of organisational capability [START_REF] Helfat | The evolution of firm capabilities[END_REF][START_REF] Helfat | Untangling dynamic and operational capabilities: strategy for the (n)ever-changing world[END_REF] to model the outcome achieved by the business process. The numbered labels on the diagram refer to key component relationships in the model that are explained below. Adaptive response. This relationship represents the requirement for "evolutionary fitness" [START_REF] Helfat | Dynamic capabilities: understanding strategic change in organizations[END_REF] on the part of the organisation's capabilities. The environment exerts pressure for the organisation to adapt its portfolio of organisational capabilities. The organisation responds to meet this selective pressure by detecting the need to change and then redesigning or redeploying its resources, including business processes, to achieve the necessary outcome. In a commercial environment, a capability such as "manufacture cars" has an evolutionary fitness that is a function of the market demand and the competitors' products. For a non-commercial business, the adaptive imperative may come from a regulatory change, for example.

2. Demand/Supply Alignment. This is the central organisational alignment relationship whereby the external demand pressure for a given capability is met (or not) by the supply side: or in other words the ability of the organisation's business processes to deliver such a capability.

3. Process Evolution. The ostensive business process is interpreted every time it is practiced by the human actor. The loop back from the performative to the ostensive means that the ostensive is not a static representation of some a priori design: instead it is a definition that moves in line with what is learned from practice. "Learned" is used in a wide sense here: it not only refers to an intentional activity but also it is the necessary by-product of performativeostensive relationship. So in this latter sense, it is inevitable that the practice will induce a drift away from the process-as-designed. This is consistent with the Feldman and Pentland's original characterisation of routines [START_REF] Feldman | Reconceptualizing Organizational Routines as a Source of Flexibility and Change[END_REF] and their more recent work on modeling this experiential learning [START_REF] Pentland | Dynamics of organizational routines: A generative model[END_REF]. This form of organisational learning confers a bottom-up adaptive capacity whereby the business processes can evolve to meet a new organisational capability need and thus enable or maintain evolutionary fitness.

4. Technology Imbrication. This represents the socio-technical relationship whereby the features available in the technology are interpreted by the user into a set of affordances [START_REF] Zammuto | Information technology and the changing fabric of organization[END_REF]. These affordances are the product of the user's particular goals, experience and skills providing a unique context for how the technology features (as designed) are perceived as part of the business process. In line with Leonardi [11], the value of the technology only emerges when there is imbrication with human agency, or in other words, there is an interdependent relationship between the two. The other side of the imbrications relationship is that technology is improvised, or worked-around, when the affordance presented by the technology is inadequate for the task at hand. These improvisations, in turn, give rise to more affordances for other actors.

2.4

Inter-Organisational View

If we look at a process-level interoperability, as is described in the FEI [START_REF] Chen | Framework for enterprise interoperability[END_REF][START_REF] Chen | Enterprise Interoperability[END_REF], a consequence of this is illustrated in Fig. 2. In the top part of the diagram (A) we have interoperating processes between the two enterprises. After some elapsed time we get a situation where the interoperability may be threatened, for example, by a constraint that is operating in the technology layer. The process, due to the human agency, adapts so as to maintain the interoperability, as shown in Fig. 2 (B). People learn to workaround constraints and their improvised solutions become "business as usual" over time. Common instances of this phenomenon are where spreadsheets are used to augment data not (or perceived not to be) catered for in corporate IT systems. These evolve to become de-facto systems of record rather than temporary fixes. In an order to cash process between a customer and supplier, the process is often sustained by the inter-relationships of human intermediaries, such as when the procurement officer in one company knows who to call in the supplier company to get prompt attention. A corollary of the unilateral "evolutionary drift" of the business processes depicted in Fig. 2, is that interoperation may stop working rather than continue to be maintained.

The important point is that in both cases adaptation has occurred through the normal interpret-agency-learn cycle, labeled (3) in Fig. 1, rather than through any intentional process redesign.

Insights for Enterprise Interoperability Frameworks

The conceptual model we have presented above contains two aspects that we argue need to be addressed in the discussion relating to enterprise interoperability, and thence in the formulation of EIFs that seek to describe and structure knowledge about this domain. The first is that there is an inherently non-deterministic component in the concept of business process that is a function of the agency of human actors. The second is that in the course of a (human-centered) business process being practiced, what the enterprise understands the process to be, or its ostensive component, will inevitably drift away from any a priori version that may have been designed. We suggest that if our objective in formulating EIFs is to provide a way to structure and thence gain insight into the interoperability problem domain, we need to understand these effects and incorporate them into the EIF ontology.

So, how might we proceed with this objective of accommodating the human agency effects into an EIF ontology? If we take the Chen and Daclin (then nascent) ontology for FEI [START_REF] Chen | Framework for enterprise interoperability[END_REF], we can see that (business) process is represented as a type of concern, which forms one of the dimensions in their EI model of the Problem Space, the other being barrier. The FEI then addresses interoperability as the solution to a problem in terms of how a concern/barrier is mapped to an approach in the Solution Space. Thus, in FEI parlance, processes across two enterprises will interoperate if all the barriers are solved. The focus is not so much conditions for EI, then, but conditions for where EI will fail. Clearly this model, as does Naudet et al. [START_REF] Naudet | Towards a systemic formalisation of interoperability[END_REF], relates to a situation at a point in time, be that a priori, when a potential EI problem can be designed for; or a posteriori, when an interoperability problem actually surfaces. What is needed to accommodate the evolutionary process we describe, is a more dynamic approach. The relationship between problem and solution is more complex than these existing models allow for. The process whereby an interoperability problem is "headed off" before it actually happens by an adaptation of the business processes involved, is an example of this type of dynamics. The concept of dynamic capability [START_REF] Teece | Dynamic capabilities and strategic management[END_REF][START_REF] Teece | The dynamic capabilities of firms: an introduction[END_REF], from the management sciences literature, provides a theoretical basis for further understanding this phenomenon. It suggests [START_REF] Schreyogg | How dynamic can organizational capabilities be? Towards a dual-process model of capability dynamization[END_REF][START_REF] Weerdt | Organizing for Flexibility: Addressing Dynamic Capabilities and Organization Design[END_REF] the existence of a second-order cybernetic capacity that performs a monitor and alignment function that operates on the first-order business process system described. This function would act to maintain the alignment of the interoperating business processes to the required outcome, characterised in terms of the required organisational capability (e.g a successful order to cash process between customer and supplier). Without this higher order function, there is no guarantee where the evolutionary process drift will lead.

Conclusion and Perspectives

In this paper, we have introduced a new theoretical element into the EI discourse, namely duality of ostensive and performative business processes that emerges from human agency. In this we have sought to stimulate discussion on how well studied organisational phenomena, drawn from management sciences and organisational sci-ence, factor into the traditionally technical realm of EI and EIFs. Some questions that arise have been touched on: how does the intrinsic adaptive nature of human-centered business processes play into EI and EIFs? When is EI "broken" at the business process level, given there is some capacity within the organisation to "fix" problems without any special intervention? Are these process-level workarounds compensating for inadequate technology? What are the implications for enterprise managers whose business model relies on process-level EI -e.g. with partner organisationsto deliver capability to their end-customers? In particular how can we apply the extant body of knowledge from the management sciences, including the theory of dynamic capabilities, to this area? We hope to investigate these issues further and to add to the burgeoning body of knowledge in this important field.

Fig. 1 .

 1 Fig. 1. -Conceptual Model of Business Process

Fig. 2 .

 2 Fig. 2. -Interoperability and process evolution


Kidiyo Kpalma

Joseph Ronsin

An Overview of Advances of Pattern Recognition Systems in Computer Vision

Introduction

First of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? In 1985, Satoshi Watanabe [START_REF] Watanabe | Pattern recognition: human and mechanical[END_REF] defined a pattern as "the opposite of chaos; it is an entity, vaguely defined, that could be given a name." In other words, a pattern can be any entity of interest which one needs to recognise and/or identify: it is so worthy that one would like to know its name (its identity). Examples of patterns are: a pixel in an image, a 2D or 3D shape, a typewritten or handwritten character, the gait of an individual, a gesture, a fingerprint, a footprint, a human face, the voice of an individual, a speech signal, ECG time series, a building, a shape of an animal. A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task. The classification scheme is usually based on the availability of the training set that is a set of patterns already having been classified. This learning strategy is termed as supervised learning in opposition to the unsupervised learning. A learning strategy is said to be unsupervised if for the system is not given an a priori information about classes; it establishes the classes itself based on the regularities of the features. Features are those measurements which are extracted from a pattern to represent it in the features space. In other words, pattern analysis enables us to use some features to describe and represent it instead of using the pattern itself. Also called characteristics, attributes or signatures the recognition efficiency and reliability are dependent on their choice.

Pattern recognition constitutes an important tool in various application domains, but unfortunately, that is not always an easy task to carry out. Commonly, one can encounter four major methodologies in PRSs; which are: statistical approach, syntactic approach, template matching, neural networks. In this chapter, our remarks and details will be directed, principally, towards systems based on the statistical approach since it is the more commonly used in practice.

Statistical approach

Typically, statistical PRSs are based on statistics and probabilities. In these systems, features are converted to numbers which are placed into a vector to represent the pattern. This approach is most intensively used in practice because it is the simplest to handle. In this approach, patterns to be classified are represented by a set of features defining a specific multidimensional vector: by doing so, each pattern is represented by a point in the multidimensional features space. To compare patterns, this approach uses measures by observing distances between points in this statistical space. For more details and deeper considerations on this approach, one can refer to [START_REF] Jain | Statistical Pattern Recognition: A Review[END_REF] that presents a review of statistical pattern recognition approaches.

Syntactic approach

Also called structural PRSs, these systems are based on the relation between features. In this approach, patterns are represented by structures which can take into account more complex relations between features than numerical feature vectors used in statistical PRSs [START_REF] Venguerov | Generalised Syntactic Pattern Recognition as a Unifying Approach in Image Analysis[END_REF]. Patterns are described in hierarchical structure composed of sub-structures composed themselves of smaller sub-structures. As explained in [START_REF] Sonka | Image Processing, Analysis and Machine Vision[END_REF], the shape is represented with a set of predefined primitives called the codebook and the primitives are called codewords. For example, given the codewords on the left of figure 1 (1)

The system parses the set of extracted features using a kind of predefined grammar. If the whole features extracted from a pattern can be parsed to the grammar then the system has recognised the pattern. Unfortunately, grammar-based syntactic pattern recognition is generally very difficult to handle.

Template matching

Template matching approach is widely used in image processing to localize and identify shapes in an image. In this approach, one looks for parts in an image which match a template (or model). In visual pattern recognition, one compares the template function to the input image by maximising the spatial cross-correlation or by minimising a distance: that provides the matching rate. The strategy of this approach is: for each possible position (in the image), each possible rotation, or each other geometric transformation of the template, compare each pixel's neighbourhood to this template. After computing the matching rate for each possibility, select the largest one, that exceeds a predefined threshold. It is a very expensive operation while dealing with big templates and/or large sets of images [START_REF] Brunelli | Template Matching: Matched Spatial Filters And Beyond[END_REF][START_REF] Roberts | Independent Component Analysis-principles and practice[END_REF][START_REF] Cole | Visual Object Recognition using Template Matching[END_REF]. Figures 2 illustrate a pattern recognition based on the template matching approach. Figure 2.a is the input image I, Fig. 1.b represents two templates (K representing letter 'K' and P letter 'P'). Figures 2.c and 2.d represent, respectively, the normalized cross-correlation of I with K and the normalized crosscorrelation of I with P. On these two images, the cross-correlation peaks surrounded by a circle indicate the location of the most matching letter in the input image. On figure 2.e, we have superposed the templates on the input image, accordingly to the coordinates of corresponding correlation peaks. For this study, we didn't take the rotation and the scaling into account: from the result, it clearly appears that this approach retrieves only the shape that matches perfectly the model (size and rotation). This explains why only one 'K' (the rotated one) and only one 'P' (the down-scaled one) are recognised.

Neural networks

Typically, an artificial neural network (ANN) is a self-adaptive trainable process that is able to learn to resolve complex problems based on available knowledge. A set of available data is supplied to the system so that it finds the most adapted function among an allowed class of functions that matches the input.

An ANN-based system simulates how the biological brain woks: it is composed of interconnected processing elements (PE) that simulate neurones. Using this interconnection (or synapse), each neurone (or PE) can pass information to another. As can be seen on figure 3, these interconnections are not necessarily binary (on or off) but they may have varying weights defined by the weight matrix W: the weight applied to a connection results from the learning process and indicates the importance of the contribution of the preceding neurone in the information being passed to the following neurone. Figure 3 shows a simple neural network representing the Perceptron as defined by Frank Rosenblatt in 1957. On this example, the output Out j (j=1 or 2) is defined by a weighted combination of the inputs. In the reference [START_REF] Abdi | A neural network primer[END_REF], the author presents a nice introduction to ANNs.

Besides these approaches, one can encounter other methodologies like those based on fuzzyset theoretic, genetic algorithms. In some applications, hybrid methodologies combine different aspects of these approaches to design more complex PRSs. In (Liu et al., 2006), the authors present an overview of pattern recognition approaches and the classification of their associated applications. In 1

In 2

In 3

Fig. 3. Example of neural network

In the remainder of this chapter, we will develop three sections. First, we present a generic scheme of a pattern recognition system. Then we give an overview of the advances of different PRSs and some examples of their applications. Last, as an illustration, we present a specific application example based on our MSGPR (Multi-Scale curve smoothing for Generalised Pattern Recognition) description method. As presented further, MSGPR is a multi-scale method we have developed for describing planar objects by analysing their boundary.

A generic scheme of a pattern recognition system

From now, our concerns will be primarily focused on PRSs in computer vision. Commonly, in this field, the input is one or more images and the output is one or more images with eventually, some semantic and/or textual entities.

In figure 4, we represent a generic scheme of a (statistical) PRS. This figure summarises the principal aspects of a PRS in computer vision. On this figure the two successive tasks can be observed: on one hand, the analysis/description task (see on figure 4) and on the other hand the classification/recognition task (see on figure 4). After features are extracted, the features selection that may follow aims at reducing the number of features to be provided to the classification process. Features that are likely to improve discrimination are retained and the others are discarded. During this processing, higher level features can be derived by combining and/or transforming low level features, e.g. by applying the so called independent component analysis (ICA) [START_REF] Roberts | Independent Component Analysis-principles and practice[END_REF]: this operation thus leads to the reduction of the dimension of the feature space. These features must be as discriminative as possible to reduce false alarms due to misclassification during the second task. Efficient features must also present some essential properties such as:

• translation invariance: whichever be the location of the pattern, it must give exactly the same features, • rotation invariance: extracted features must not vary with the rotation of the pattern, • scale invariance: scale changing must not affect the extracted features, • noise resistance: features must be as robust as possible against noise i.e. they must be the same whichever be the strength of the noise that affects the pattern, • statistically independent: two features must be statistically independent, • compact. The number of retained features is not too large. It must also be fast in extraction time and in matching, • reliable: as long as one deals with the same pattern, the extracted features must remain the same.

Image sensor

Features extraction

Features selection

Analysis/Description

Similarity measure (matching)

Classification/Recognition

Interpretation

Off-line Learning Models' Features database

Images database

Fig. 4. A generic PRS scheme

During the classification task, the system uses the features extracted in the analysis stage from each of the patterns to compare. As illustrated on figure 4, features are extracted from the patterns of the database during an off-line learning processing. This enables to create features database before each query occurs: by proceeding this way, one doesn't need to compute features of models at each query. To compare two patterns, the system uses a metric that measures a kind of distance (the similarity or the dissimilarity) to assess how similar are two patterns: it is an expression of the distance between the points representing the two patterns in the features space. This procedure gives the similarity index or similarity score between two patterns. In some cases (probably the most natural way), the similarity index is given in terms of a rate varying from 0% for totally different patterns to 100% for perfectly similar patterns [START_REF] Kpalma | Multiscale contour description for pattern recognition[END_REF]. Some commonly used metrics are Minkowski distance, cosine distance, Hausdorff distance, Mahalanobis Distance, [START_REF] Veltkamp | State-of-the-art in shape matching[END_REF][START_REF] Zhang | Image Retrieval Based on Shape[END_REF] or city block distance and Euclidian distance that are particular Minkowski distances. The following paragraph illustrates formalism of some of them.

Let V A (a 1 , a 2 ,…, a N ) and V B (b 1 , b 2 ,…, b N )
∑ = - = N 1 i i i B A 1 b a ) V , V ( d ( 2
)
Euclidian distance (d 2 ) ( ) ∑ = - = N 1 i 2 i i B A 2 b a ) V , V ( d (3) Cosine distance (d 3 ) ( ) ( ) ∑ ∑ ∑ = = = × × - = × - = θ - = N 1 i 2 i N 1 i 2 i N 1 i i i B A T B A B A 2 b a b a 1 V V V V 1 ) cos( 1 ) V , V ( d (4)
where θ is the angle between the two vectors V A and V B . Figure 5 shows an example of three vectors V, U and W represented in 2D space. As it can be seen on this example the value of the similarity/dissimilarity depends on the used distance (metric). In the tables on this figure, d 3 gives the same distance between U and W, on one hand, and between V and W, on the other hand, (d 3 (U,W)= d 3 (V,W)=0.15) but it gives 0 distance between U and V. This leads to confusions, because a distance of 0 that also means vectors equality, may lead to the decision that the patterns to be compared are the same. A particular attention must be paid while choosing a distance. In [START_REF] Kpalma | Multiscale contour description for pattern recognition[END_REF] we have proposed a cosine-based distance that enables to remove the ambiguity of the distance between collinear vectors. Since the obtained distance varies from a metric to another, one must be very careful and be sure to use the same metric during all the procedure.

Pattern recognition applications and an overview of advances

Pattern recognition is studied in many fields, including psychology, ethnology, forensics, marketing, artificial intelligence, remote sensing, agriculture, computer science, data mining, document classification, multimedia, biometrics, surveillance, medical imaging, bioinformatics and internet search. Pattern recognition helps to resolve various problems such as: optical character recognition (OCR), zip-code recognition, bank check recognition, industrial parts inspection, speech recognition, document recognition, face recognition, gait recognition or gesture recognition, fingerprint recognition, image indexing or retrieval, image segmentation (by pixels classification)... In (Pal & Pal, 2002; …) number of experts address the problem of pattern recognition and present basic concepts involved. One can find the evolution of pattern recognition ; this enables the reader to establish a categorisation of the existing PRSs according to the used methodology and the application. In [START_REF] Kuncheva | Classifier Ensembles for Changing Environments[END_REF], the author addresses the non-trivial concept of forgetting in the challenging field of machine learning in non-stationary changing environments. This point of view is essential in on-line diagnosis when using medical imaging: indeed while dealing with PR in real world, the pattern being studied is subject to variation with respect to time. A possible solution is to continuously update the classifier. By doing so, the classifier must be able to "forget" the outdated knowledge. The idea behind this concept is to design an adaptive training system that is able to self-adapt itself accordingly to the changing of the pattern being studied. Pattern recognition is also applied in more complex fields like data mining (DM) also called knowledge-discovery in databases (KDD). This emerging topic includes the process of automatically searching large volumes of data for patterns such as association rules. As defined in [START_REF] Frawley | Knowledge Discovery in Databases: An Overview[END_REF], the DM "is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Given a set of facts (data) F, a language L, and some measure of certainty C, we define a pattern as a statement S in L that describes relationships among a subset F S of F with a certainty c, such that S is simpler (in some sense) than the enumeration of all facts in F S . A pattern that is interesting (according to a user-imposed interest measure) and certain enough (again according to the user's criteria) is called knowledge. The output of a program that monitors the set of facts in a database and produces patterns in this sense is discovered knowledge.

Pattern recognition in robotics

The applications of PRSs in robotics are permanent. More recently, Mario E. Munich and his co-authors [START_REF] Munich | Application of Visual Pattern Recognition to Robotics and Automation[END_REF] have presented a summary on this subject. In this paper, they show that recent advances in computer vision have given rise to a robust and invariant visual pattern recognition technology based on extracting a set of characteristic features from an image. With visual pattern recognition systems, a robot may acquire the ability to explore its environment without user intervention ; it may be able to build a reliable map of the environment and localize itself in the map: this will help the robot achieve full autonomy. Examples of robots using visual pattern recognition approaches are the Sony's AIBO ERS-7, Yaskawa's SmartPal, and Phillips' iCat. In robotics, visual servoing or visual tracking is of high interest. For example visual tracking allows, robots to extract themselves the content of the observed scene as a human observer can do it by changing his different perspectives and scales of observation. François Chaumette [START_REF] Chaumette | Visual servoing using image features defined upon geometrical primitives[END_REF], has addressed the problem and proposed some solutions in a closed loop system based on vision-based task. In [START_REF] Chaumette | Image Moments: A General and Useful Set of Features for Visual Servoing[END_REF], he proposes various visual features based on the image moments to characterise planar objects in visual-servoing schemes.

Pattern recognition in biometrics

The biometric authentication takes increasing place in various applications ranging from personal applications like access control to governmental applications like biometric passport and fight against terrorism. In this applications domain, one measures and analyses human physical (or physiological or biometric) and behavioural characteristics for authentication (or recognition) purposes. Examples of biometric characteristics include fingerprints, eye retinas and irises, facial patterns and hand geometry measurement, DNA (Deoxyribonucleic acid). Examples of biometric behavioural characteristics include signature, gait and typing patterns. This helps to identify individual people in forensics applications. Reference (Jain et al., 2004a) is an interesting starting point to pattern recognition approaches and systems in biometric. This paper gives a brief overview of the field of biometrics and summarizes some of its advantages, disadvantages, strengths, limitations, and related privacy concerns. In [START_REF] Jain | Biometrics: A Grand Challenge[END_REF], the authors also address the problem of the accuracy of the authentication and that of the individual's right to the security, to the privacy and to the anonymity. The reader is encouraged to have a look on the article presented in [START_REF] Jain | A Touch of Money[END_REF]. The authors of this article address a problem of identity steeling through a true story and then they present some current or forthcoming systems based on biometric PRSs that will help prevent identity steeling.

Content-based image retrieval

Content-based image retrieval systems aim at automatically describing images by using their own content: the colour, the texture and the shape or their combination. As explained in [START_REF] Sikora | The MPEG-7 Visual Standard for Content Description-An Overview[END_REF][START_REF] Bober | MPEG-7 Visual Shape Descriptors[END_REF], image retrieval has became an active research and development domain since the early 1970s. During the last decade the research on image retrieval became of high importance. The most frequent and common means for image retrieval is to index them with text keywords. If this technique seems to be simple, it becomes rapidly laborious and fastidious while facing large volumes of images. On the other hand, images are rich in content so, to overcome difficulties due to the huge data volume, the content-based image retrieval emerged as a promising mean for retrieving images and browsing large images databases With the simultaneous rapid growth of computer systems and the growing huge availability of digital data, such pattern recognition systems become increasingly necessary to help browse databases and find the desired information within a reasonable time limit. Accordingly to this observation, systems like CBIR (Content-Based Image Retrieval), QBIC (Query By Image Content), QBE (Query By Example) need more attention and take more and more place in the concerns of the researchers [START_REF] Mokhtarian | Efficient and Robust Retrieval by Shape Content through Curvature Scale Space[END_REF][START_REF] Trimeche | Similarity Retrieval of Occluded Shapes Using Wavelet-Based Shape Feature[END_REF][START_REF] Veltkamp | Content-based retrieval systems: a survey[END_REF][START_REF] Veltkamp | State-of-the-art in shape matching[END_REF]). With query by example, the user supplies a query image and the PRS finds images of the database that are most similar to it based on various low-level features like colour, texture or shape. With query by sketch, the user draws roughly the image he is looking for and the PRS locates images of the database that match the best the sketch. In the reference [START_REF] Veltkamp | Content-based retrieval systems: a survey[END_REF], are reported various CBIR systems. After a brief description of CBIR system, the authors present different kinds of existing systems along with the features involved. In the context of image indexing, CBIR systems use content information as summarised in figure 6. An image can then be described by using features derived from colour, texture, shape or a combination of those features.

Input image

Colour-based features

Colour features are based on colour distribution inside the image. There are many approaches to define colour-based features: colour space dominant, colour histogram or colour space. Various colour representation space exist: red-green-blue (RGB) space, huesaturation-value (HSV) space or those based on the international commission on illumination (or CIE: commission internationale de l'éclairage) CIELUV space, CIELAB space, CIEXYZ space. From these representation features are defined based the colour histograms.

There are different types of colour histograms depending on how the colour space is partitioned. The fixed binning for all images based on scalar linear quantisation, the adaptive binning based on an adaptive quantisation and the clustered binning based on the concept of vector quantisation. Some particular distances between histograms or main modes of histograms are used to measure the similarity/dissimilarity between colour histograms: Euclidian distance, histogram quadratic distance, histogram intersection distance [START_REF] Smith | Tools and Techniques for Color Image Retrieval[END_REF], Jeffrey divergence, Kullback-Leibler divergence earth mover's distance. In the current description of the colour within MPEg-7, the following colour spaces are supported: RGB, YCrCb, HSV, hue-min-max-difference (HMMD), Linear transformation matrix with reference to RGB and monochrome (Martinez, 2004).

Texture-based features

For each pixel of the image, one can determine the histogram of grey levels in predefined neighbouring region centred on that pixel. Distribution of pairs of grey levels for a given spatial relation on pixels can be observed in co-occurrence matrix M(i,j) [START_REF] Haralick | Textural features for image classification[END_REF].

Examples of various grey level co-occurrence matrices (GLCM) features defined by Haralick are based on these co-occurrence matrices. In Table 1, by considering a textured image with grey levels ranging from 0 to L-1, we present some of these texture features.

Angular Second Moment

( ) 2 1 L 0 j 1 L 0 i j , i M ASM ∑ ∑ - = - = = Contrast ( ) j , i M ) j i ( C 1 L 0 j 2 1 L 0 i ∑ ∑ - = - = - = Inverse Difference Moment ∑ ∑ - = - = - + = 1 L 0 j 2 1 L 0 i ) j i ( 1 ) j , i ( M IDM Homogeneity ∑ ∑ - = - = - + = 1 L 0 j 1 L 0 i j i 1 ) j , i ( M H Entropy )) , ( ( ln ) , ( 1 0 1 0 j i M j i M E L i L j ∑∑ - = - = - =
Table 1. Example of textures features

Shape-based features

There are many approaches [START_REF] Coster | Précis d'Analyse d'Images[END_REF][START_REF] Kpalma | Caractérisation de textures par l'anisotropie de la dimension fractale[END_REF][START_REF] Sossa | Object Recognition, Summer School on Image and Robotics[END_REF], to estimates some properties of the shapes. We present, below, some samples of these properties. Figure 7 shows various shapes and the corresponding measures of their properties.

The elongation (EL) indicates how long is the pattern relatively to its width. It is defined by the following expression:

M m 100 EL λ λ =
(5) λ m and λ M being, respectively, the smallest and the largest eigenvalues of the inertia matrix of the shape. Also called elongation factor or elongation coefficient, this parameter varies from 0% for long but thick shapes to 100% for isotropic shape (see Fig. 7.e and Fig. 7.f).

The compactness measures how branchy or how tortuous is the shape. For a given 2D shape, let A be the enclosed area and P the perimeter ; the compactness is defined by:

2 4 100 P A CO π = (6)
The compactness varies from 0% for very branchy or very tortuous shapes to 100% for compact shapes like a circle (see Fig. 7.a and Fig. 7.c).

The mass deficit coefficient (MD) measures the area variation between the shape and the minimum enclosing circle centred on the centre of gravity of the shape. For a shape with area A, let S C be the area of the circumscribed circle, then the mass deficit area is defined as follows:

C C S A S 100 MD - = (7)
The mass excess coefficient (ME) measures the area variation between the shape and the maximum enclosed circle centred on the centre of gravity of the shape. For a shape with area A, let S I be the area of the inscribed circle, then the mass deficit area is defined as follows:

A S A 100 ME I - = (8)
The two previous parameters, give another estimation of the compactness: they vary from 0% for compact shapes (e.g. a cercle) to 100% for spread out tortuous patterns (see Fig. 7.a and Fig. 7.d)

The isotropic factor (IF) tells how isotropic is the pattern: it indicates how regular is the shape around its centre of gravity. For a given 2D shape, let R m be its minimal radius and R M its maximal radius then the IF parameter is defined by:

M m R R 100 IF = (9)
The isotropic factor varies from 0% for anisotropic shapes to 100% for isotropic shapes like a circle (see Fig. 7.a and Fig. 7

Fig. 7. Various shapes and examples of shape-based features

In the context of shape description, D. Zhang summarized very well the situation [START_REF] Zhang | Review of shape representation and description techniques[END_REF]. Figure 8 shows the flowchart of shape description approaches in a pattern recognition system. Typically, there are two kinds of approaches in shape description: the contour-based approach and the region-based one.

Shape description

Region-based features

Scale space descriptors

Wavelet descriptors

Fig. 8. A classification of shape description approaches

Contour-based approach

Contour-based approaches extract shape features from the only contour in two possible ways: structural or global. In the structural approach, the contour is divided into sub-section to generate strings or trees according to a particular syntax. The similarity between two shapes is then measured by matching their strings or their trees. While dealing with the contour in the global way, an appropriate technique is used to extract primitive features from the integral contour: eccentricity, perimeter, circularity… From these basic features, one defines a multidimensional vector representing the shape in the features space. From this representation, the similarity measure or the matching of two shapes is done by directly measuring a specific distance between their feature vectors. For contour-based shape description, MPEG-7 working group [START_REF] Bober | MPEG-7 Visual Shape Descriptors[END_REF]Martinez, 2004) has selected the so-called Curvature Scale-Space (CSS) representation which is proved to capture perceptually meaningful features of the shape [START_REF] Mokhtarian | Efficient and Robust Retrieval by Shape Content through Curvature Scale Space[END_REF][START_REF] Matusiak | Planar Closed Contour Representation by Invariant Under a General Affine Transformation[END_REF]Lindenberg, 1998 ;[START_REF] Mokhtarian | Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standardization[END_REF]. A CSS image, represented on figure 9, is a multi-scale organization of the invariant local features of a 2-D contour: it consists of the curvature zero-crossing points recovered from the contour at multiple scales of resolution. The features extracted from the CSS image consist of the coordinates of the peaks of the CSS image. Scale decreasing is obtained through progressive low-pass filtering by convolutions of a parametric representation of the contour data with Gaussian filters of increasing width. This representation carries a number of important properties, such as: • it captures very well characteristic features of the shape, enabling similarity-based retrieval, • it reflects properties of the perception of human visual system and offers good generalization, • it is robust to non-rigid motion, • it is robust to partial occlusion of the shape, • it is robust to perspective transformations, which result from the changes of the camera parameters and are common in images and video, • it is compact. Some of the above properties of this descriptor are illustrated in figure 11, each frame containing very similar images according to CSS, based on the actual retrieval results from the MPEG-7 shape database. In figure 9, we represent two shapes and their corresponding CSS images. On the CSS images (bottom row) we have superposed the peaks points that are used to generate features [START_REF] Mokhtarian | Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standardization[END_REF].

Region-based approach

In region-based approaches, all pixels surrounded by the shape boundary are taken into account to generate the shape descriptor. Like in the case of contour-based approaches, we encounter the same two different ways in region-based shape description: global and structural one. In the structural approach, the shape is decomposed into sub-regions to generate a tree to represent the shape. In the global way, one computes some characteristic features to generate a vector to represent the shape. Common global features derived from a region-based approach are: geometrical moment invariants, shape matrix, area, compactness, eccentricity, Euler number, geometric moments, Legendre moments, Zernike moments… For region-based shape description, the MPEG-7 working group [START_REF] Bober | MPEG-7 Visual Shape Descriptors[END_REF]Martinez, 2004) has selected the angular radial transform (ART). It is a moment-based approach for a 2D region-based shape description. In [START_REF] Ricard | Generalizations of angular radial transform for 2D and 3D shape retrieval[END_REF] the authors proposed a generalization of the ART approach to describe 2D and 3D shapes for contentbased image retrieval purpose. The contour-based approaches are more appealing than region-based approaches because they involve less computation complexity, than the region-based ones, with enough discriminating efficiency. It is also demonstrated that characteristic information about a shape lie essentially on its contour features. The main drawback of contour-based descriptors is that they are more subject to noise and variations than region-based ones.

Figure 10 shows examples of shapes and illustrates situations for which the contour-based or the region-based descriptors are most suitable. A shape may consist of just one single region (see Fig. 10.a-c) or a set of several regions as well as regions with some holes inside them as illustrated in figures 10.d-f. Since the regionbased descriptors make use of all pixels constituting the shape, they can describe any kind of shapes. They are more suitable than the contour-based descriptors to handle complex shape consisting of holes in the object or several disjoint regions (see Fig. 10.d-f) in a single descriptor. Indeed, for contour-based descriptors, these shapes consist not of a single contour but of multiple contours leading, thus, to multiple descriptors. • the region-based shape descriptor will consider 10.g and 10.h similar but different from 10.i, • the contour-based shape descriptor will consider 10.h and 10.i similar but different from 10.g. As illustrated by MPEG-7 (Martinez, 2004), a challenge for a pattern descriptor is to enable the recognition of a pattern even if it has undergone various deformations namely partial occultation (Fig. 11.a) and non-rigid deformation (Fig. 11.b). Figure 11.a, according to (Martinez, 2004), illustrates the robustness to partial occultation: indeed, in this figure, one can note that the tails or the legs of the horses are sometimes occulted but they are recognised to be from the same class. As presented in [START_REF] Mokhtarian | Silhouette-Based Occluded Object Recognition through Curvature Scale Space[END_REF][START_REF] Petrakis | Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming[END_REF] The choice of a description method will depend on the application so, sometimes, one needs to make a compromise. Nevertheless, MPEG-7 has set some essential principles to evaluate the suitability of shape descriptor: retrieval accuracy, compactness, generics, low computation complexity, robustness and the ability to represent a shape in hierarchical way: from coarse to fine representation.

(d) (f) (b) (e) (h) (c) (i) (a) (g)

An overview of the advances in pattern recognition

Remco C. Veltkamp and Mirela Tanase presented in (Veltkamp & Tanase, 2000) a large panel of CBIR systems. Various approaches of the state of the art in content-based image retrieval and video retrieval are explored along with the features used in each approach, they also describe the matching functions used. This overview enables to confirm, as it was said before, that commonly designed CBIR systems are generally based on visual features such as colour, texture and shape.

In [START_REF] Iqbal | CIRES: A System for Content-based Retrieval in Digital Image Libraries[END_REF] is presented CIRES (Content-based Image REtrieval System), an online system for retrieval in image libraries. It is done to extend the retrieval paradigm, which was mostly limited to colour and texture analyses, by using image structure. Image structure was extracted via hierarchical perceptual grouping principles.

In [START_REF] Mittal | An Overview of Multimedia Content-Based Retrieval Strategies[END_REF] the author presents an overview of the content-based retrieval along with different strategies in terms of syntactic and semantic indexing for retrieval. After an analysis of the matching techniques used and the learning methods the author addresses some directions for future research in the content-based retrieval domain. Recently, N. Snavely and co-authors [START_REF] Snavely | Photo tourism: Exploring photo collections in 3D[END_REF]) have presented a system that consists of 3D image-based modelling and representation of an unorganised images taken by different cameras in different conditions. The challenging aim of the system is to use the content-based information to browse an image database and reply to questions like:

• "where was I? Tell me where I was when I took this picture" • "what am I looking at? Tell me about objects visible in this image by transferring annotations from similar images" To do this, they used the SIFT (Scale Invariant Feature Transform) keypoints detector that was shown to be transformation invariant [START_REF] Lowe | Distinctive image features from scale invariant keypoints[END_REF]. Among the various forthcoming systems, we can encounter MPEG-7. Formally named "Multimedia Content Description Interface", MPEG-7 aims at managing data in the way that content information can be retrieved easily. It is under development by the Moving Picture Coding Experts Group (MPEG) that is a working group of ISO/IEC ( * ) standards organization. It is in charge of the development of international standards for video and/or audio compression, decompression, processing and representation. This group has also developed well-known standards that are MPEG-1, MPEG-2 and MPEG-4. MPEG-1, MPEG-2 and MPEG-4 also make content available but MPEG-7 enables to find the desired content. MPEG-7 visual description tools consist of basic structures and descriptors that cover basic visual features: colour, texture, shape, motion, localization. Each category consists of elementary and sophisticated descriptors [START_REF] Sikora | The MPEG-7 Visual Standard for Content Description-An Overview[END_REF][START_REF] Bober | MPEG-7 Visual Shape Descriptors[END_REF]. One must note that MPEG-7 addresses many different applications in various environments, thus it needs to provide a standard flexible and extensible framework for describing audio-visual data.

Application example based on the MSGPR method

In [START_REF] Kpalma | Multiscale contour description for pattern recognition[END_REF] we have presented an original pattern description approach based on the multi-scale analysis of the contour of planar objects. This proposed approach summarises the different presented considerations in this chapter. It is well known that some objects, especially natural ones, exists with a more or less large range of scales; and that the aspect of the object can change from one scale to another. Without a priori information about the distance of observation inside a given scene, an interesting challenge can be: to found an object without any precision about its scale of observation. Faced with this situation, it is very difficult to significantly describe a pattern using only one meaningful scale. To overcome this problem, increasingly more pattern description techniques are based on multi-scale or multiresolution representation methods [START_REF] Lindeberg | Principles for Automatic Scale Selection[END_REF]. Within this context, methods based on the pattern itself [START_REF] Torres-Méndez | Translation, Rotation, and Scale-Invariant Object Recognition[END_REF][START_REF] Kadyrov | Object descriptors invariant to affine distortions[END_REF][START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF][START_REF] Grigorescu | Distance Sets for Shape Filters and Shape Recognition[END_REF] exist as well as methods based on pattern contour behaviour [START_REF] Matusiak | Planar Closed Contour Representation by Invariant Under a General Affine Transformation[END_REF][START_REF] Roh | 2-D object recognition using invariant contour descriptor and projective refinement[END_REF][START_REF] Wang | Multiscale curvature-based shape representation using B-spline wavelets[END_REF][START_REF] Latecki | Shape Descriptors for Non-rigid Shapes with a Single Closed Contour[END_REF]. This study deals exclusively with methods based on the pattern contour. Called MSGPR (A Multi-Scale curve smoothing for Generalised Pattern Recognition) this scale-space [START_REF] Mokhtarian | Efficient and Robust Retrieval by Shape Content through Curvature Scale Space[END_REF][START_REF] Matusiak | Planar Closed Contour Representation by Invariant Under a General Affine Transformation[END_REF][START_REF] Wang | Multiscale curvature-based shape representation using B-spline wavelets[END_REF][START_REF] Mokhtarian | Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standardization[END_REF] method is based on multi-scale smoothing of a planar pattern contour. This method is ( * ) ISO/IEC stands for International Standards Organization/International Electro-technical Committee.

(1) [START_REF] Torres-Méndez | Translation, Rotation, and Scale-Invariant Object Recognition[END_REF] (3) (4) (5) (6) [START_REF] Torres-Méndez | Translation, Rotation, and Scale-Invariant Object Recognition[END_REF] (3)

(4) (5) (6) b) a) (2) (3) (4) (5) (6) (8) (7) (1) (1) (2) (3) (4) (5) (6) (7) (8) d) c)

Features definition and selection

After generating the IPM function, the following stage, but not the least one is features definition and selection. In [START_REF] Kpalma | Multiscale contour description for pattern recognition[END_REF] we used the circular distance between IPM points at various scale values. To extract these characteristic features, we first set the scale parameter σ to σ 0 value (e.g. σ 0 = 180). Then, for each pattern:

V = (V 0 , V 1 ,…, V M-1 ) (12)

Similarity measure

To measure the matching rate between two V A and V B attribute vectors associated to two patterns, we define a similarity function as follows:

) V , V ( Max ) V , V ( Min )) cos( 1 ( 50 ) V , V ( SimScore B A B A B A γ + = ( 13
)
where γ is the angle between both vectors and where . indicates the module of a vector. This function ranges from 0% for very different vectors to 100% for perfectly matching vectors.

Application to car plate character recognition

In this section, we present a system we have developed to illustrate pattern recognition systems. This application can be classified into the group of the contour-based statistical approaches. Our application illustrates an automatic reading of the number plates by using their digital images. Applying the IPM-based features we carry out the automatic recognition of the characters of the number plate. Figure 16 shows two images of plates written with different fonts: the difference appears more clearly for digit '3' out of the two plates.

Character recognition procedure

The recognition procedure is carried out into three stages as depicted in figure 17: (I) edge (or contours) detection that will enable to obtain contours delimiting each character in the image (Fig. 18.a et Fig. 18.b). One must note that this stage is very important in our process, because, the effectiveness of character recognition will depend on it.

(II) contour extraction: in this stage, one considers only the external (or the outer) boundary (Fig. 18.c), because only these contours are taken into account. As for the stage (I), one must pay particular care to the extraction of the characters so that they are continuous and closed, without self-intersection.

(III) character recognition: at this last stage, we apply our IPM-based description approach to extract the features and to integrate them into the identification process to measure the similarity score between each extracted character and the models of the data base. In this application, the similarity measure is based on the SimScore function defined by equation ( 13). As can be seen on these figures, the identification of different characters is effective enough: for each query, the identified character (the most similar: the character next to the query in figures 19.a-d) is exactly the required character. Thus, for the query '3', we identify the letter '3' with a similarity score of 79%. Table 2 summarises the three highest similarity scores for the contours presented on figure 19. For the contour '9' as a query, we retrieved the digit '9' with a similarity score up to 96% followed by the digit '6' with a similarity score of 79%. One can notice that the contour '6' of the used font is not other than the contour '9' which underwent a rotation of 180°: this explains that the digit '6' occupies the second position during the retrieval process. In the same way, the topological similarity between the digit '5' and the letter 'S' or between the digit '8' and the letter 'B' results in the appearance of 'S' and 'B', respectively, into the second position in the retrieval ranging. In spite of this topological similarity, specific properties of each character lead to sufficiently important variations of similarity scores to avoid mistakes.

Query

Retrieved character (Similarity score) '3' '3' (79%) 'C' (62%) 'E' (56%) '5' '5' (72%) 'S' (58%) '6' (55%) '8' '8' (91%) 'B' (63%) '1' (61%) '9' '9' (96%) '6' (79%) 'K' (76%) Table 2. Retrieved characters and the corresponding similarity scores.

Conclusion

As mentioned pattern recognition does not appear as a new problem. A lot of studies have been performed on this scientific field and a lot of works are currently developed. Pattern recognition is a wide topic in machine learning. It aims to classify a pattern into one of a number of classes. It appears in various fields like psychology , agriculture, computer vision, robotics , biometrics… With technological improvements and growing performances of computer science, its application field has no real limitation. In this context, a challenge consists of finding some suitable description features since commonly, the pattern to be classified must be represented by a set of features characterising the pattern. These features must have discriminative properties: efficient features must be affined transformations insensitive. They must be robust against noise and against elastic deformations due, e.g., to movement in pictures.

Through the application example based on our MSGPR method, we have illustrated various aspects of a PRS. With this example, we have illustrated the description task that enabled us to extract multi-scale features from the generated IPM function. By using theses features in the classification task, we identified the letters from a car number plate so that we automatically retrieved the license number of a vehicle. The research topic of pattern recognition is under continuous development and in perpetual progress. With the large volumes of digital images, the challenge for pattern recognition in computer vision is now the development of a CBIR-like system: system that is able to retrieve useful information by using the only content of the input image. With the growing huge availability of digital images, pattern recognition takes more and more place in our daily life to help us while browsing and looking for the desired information in a reasonable time limit. Pattern recognition appears as integrated into the forthcoming standard MPEG-7 via indexing approaches. Such standardization does not bring restriction to a domain: it gives synergy of best actors mixing challenge and cooperation. And moreover international standardization occurs as a requirement from different applications so it meets all conditions for large diffusion. Standards use the possibilities of last technological developments, and drive strong investments and focus research on the concerned domain.

As it has been observed, for example, for coding when it was integrated inside different MPEG standards, the integration of pattern recognition inside MPEG-7 will boost its last developments.

  , the shape on the right of the figure can be represented as the following string S, when starting from the pointed codeword on the figure: S = d b a b c b a b d b a b c b a b

Fig. 1 .

 1 Fig. 1. Example of syntactic description features

Fig. 2 .

 2 Fig. 2. Illustration of the template matching method

Fig. 5 .

 5 Fig. 5. Examples of similarity measures between two vectors depending on the chosen metric

Fig. 6 .

 6 Fig. 6. Content-based image description features

Fig. 9 .

 9 Fig. 9. Example of contours (top row) and the corresponding CSS image with the peaks points (bottom row)

Fig. 10 .

 10 Fig. 10. Examples of various shapes Figures 10.g-i show very similar shapes from images of a same cup. They only differ by the handle: shape 10.g has a crack at the lower handle while the handle in 10.i is filled. When comparing these shapes:

  Fig. 11. a) robustness to partial occlusion, b) robustness to non-rigid deformation, c) perceptual similarity among different shapes

Fig. 14 .

 14 Fig. 14. Example of the IPM function

  • we consider the IPM points at the set σ 0 and select two consecutive p a and p b points which are, circularly, the furthest apart in the IPM function as illustrated in figure15,• we determine the circular distance between both points to produce the first d 1 component of the V 0 features vector, • the next components of V 0 are distances coming after d 1 :V 0 = (d 1 , d 2 ,…, d N-1 , d N ) (11)To benefit from multi-scale information of the IPM function, we can define a set of M values of σ (σ 0 , σ 1 , ...,σ M-1 ) and determine the Vi feature vectors (i=0, 1, 2,…, M-1) corresponding to the σ i scales. The global V features vector is then produced by a concatenation of the individual Vi scale vectors as follows:

Fig. 15 .

 15 Fig. 15. Example of the IPM function

  Fig. 16. Examples of number plates images

Fig. 17 .

 17 Fig. 17. An overview of an automatic number plate reading system.

  be the features vectors representing patterns A and B in a N-dimensional features space ; examples of distances are defined by the following expressions.

	City block distance (d 1 )

  .d).

	EL=100.0%	EL=100.0%	EL=100.0%	EL=100.0%	EL= 44.4%	EL=100.0%
	CO=100.0%	CO= 59.6%	CO= 64.5%	CO= 9.8%	CO= 75.4%	CO= 78.5%
	MD= 0.0%	MD= 11.6%	MD= 3.8%	MD= 50.2%	MD= 41.2%	MD= 36.3%
	ME= 0.0%	ME= 4.3%	ME= 10.9%	ME= 77.3%	ME= 47.6%	ME= 21.4%
	IF=100.0%	IF= 92.0%	IF= 92.6%	IF= 33.7%	IF= 55.5%	IF= 70.7%
	a)	b)	c)	d)	e)	f)

Global Area Compactness Eccentricity Euler Number Geometric Moments Legendre Moments Shape Matrix Zernike Moments Structural Convex Hull Media Axis Core Contour-based features Structural B-Spline Chain code Invariants Polygons Global Compactness Eccentricity Circularity Elastic matching Elongation Fourier descriptors

totally translation and rotation insensitive and as showed in the initial studies it is also robust against scale change for a large range of scaling and resistant to additive noise.

Description of the MSGPR method

The framework of the MSGPR can be broken down into four main stages as follows (see Fig. 12):

1. the input contour is separated into two parameterised functions, 2. both functions are low-pass filtered (smoothed), 3. scale adjustment is then applied to both filtered functions so that the corresponding smoothed contour has the same scale as the input one, 4. finally, the intersection points map (IPM) is generated by detecting the intersection points of the input contour and the smoothed scale-adjusted one.

Coordinate separation

The input contour is represented by a series of points defined by their (x,y) coordinates. First, the input contour is separated into two functions x(u) and y(u) which are functions of the normalised curvilinear u parameter that varies from 0 to 2π relative to the curve length. Each point of the curve is then represented by its parameterised coordinates

Curve smoothing

Functions x(u) and y(u) are then gradually smoothed by decreasing the filter bandwidth. Similarly to the curvature scale space (CSS) method [START_REF] Mokhtarian | Efficient and Robust Retrieval by Shape Content through Curvature Scale Space[END_REF][START_REF] Matusiak | Planar Closed Contour Representation by Invariant Under a General Affine Transformation[END_REF][START_REF] Wang | Multiscale curvature-based shape representation using B-spline wavelets[END_REF][START_REF] Mokhtarian | Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standardization[END_REF] or other scale-space methods, smoothing is based on the Gaussian filters g(σ,u) with standard deviation σ: Since the bandwidth is conversely proportional to σ, it is clear that the bandwidth decreases as σ increases. Thus the filter cuts increasingly lower so that the output functions move towards their mean values when σ tends towards infinity.

Scale adjustment

After low-pass filtering, the scale adjustment system stretches the output contour so that it reaches the same scale as the input one and so that both contours intersect at certain points.Figure 13 shows an example of a contour and two smoothed ones (σ=10 and σ=180) after they have been scale-adjusted. The input contour C 0 and smoothed scale-adjusted contours C GC (σ) are now on the same scale so that they can intersect.

Definition of the IPM function

By increasing σ, the output contour moves towards a convex curve that has some intersection points with the input contour. By marking these intersection points for each σ, we obtain the intersection points map (IPM) function defined below which characterises the pattern.

After the scale adjustment system, the IPM function is generated as follows. For each σ value, we define a function which is an image in the scale-space (u,σ) plane so that (see Fig. 14):

point is an intersection point between the original curve and the filtered scale-adjusted one,

is not an intersection point.

Figure 14 shows examples of contours (left column) and the corresponding IPM functions (right column). On this figure, intersection points are indicated by (1) through ( 6) or (8), for the contour in Fig. 14.a or for that in Fig. 14.c, respectively. On the right column, one can see the marks corresponding to those intersection points in the IPM representation. As can be seen on this figure, the IPM function is characteristic of the contour it is derived from.