Towards an auto-adaptive vision system based on multi-agents systems.

Il existe une multitude de traitements d'images dans la littérature, chacun étant adapté à un ensemble plus ou moins grand de cadres d'application. Les traitements d'images sont fondamentalement trop différents les uns par rapport aux autres pour être mis en commun de façon naturelle. De plus, ces derniers sont trop rigides pour pouvoir s'adapter d'eux mêmes lorsqu'un problème non prévu à l'avance par le concepteur apparaît. Or la vision est un phénomène autoadaptatif, qui sait traiter en temps réel des situations singulières, en y proposant des traitements particuliers et adaptés. Elle est aussi un traitement complexe des informations, tant ces dernières ne peuvent être réduites à des représentations réductionnistes et simplifiantes sans être mutilées.Dans cette thèse, un système de vision est entrepris comme un tout où chaque partie est adaptée à l'autre, mais aussi où chaque partie ne peut s'envisager sans l'autre dans les tensions les plus extrêmes générées par la complexité et l'intrication des informations. Puisque chaque parcelle d'information joue un rôle local dans la vision, tout en étant dirigée par un objectif global peu assimilable à son niveau, nous envisageons la vision comme un système où chaque agent délibère selon une interférence produite par le potentiel décisionnel de chacun de ses voisins. Cette délibération est entreprise comme le résultat produit par l'interférence d'une superposition de solutions. De cette manière, il émerge du système à base d'agents une décision commune qui dirige les actions locales faites par chaque agent ou chaque partie du système. En commençant par décrire les principales méthodes de segmentation ainsi que les descripteurs de formes, puis en introduisant les systèmes multi-agents dans le domaine de l'image, nous discutons d'une telle approche où la vision est envisagée comme un système multi-agent apte à gérer la complexité inhérente de l'information visuelle tant en représentation qu'en dynamisme systémique. Nous encrons dans ces perspectives deux modèles multi-agents. Le premier modèle traite de la segmentation adaptative d'images sans calibration manuelle par des seuils. Le deuxième modèle traite de la représentation de formes quelconques à travers la recherche de coefficients d'ondelettes pertinents. Ces deux modèles remplissent des critères classiques liés au traitement d'images, et à la reconnaissance de formes, tout en étant des cas d'études à développer pour la recherche d'un système de vision auto-adaptatif tel que nous le décrivons.Although several image processing approaches exist, each of them was introduced in order to be used in a specific set of applications. In fact, image processing algorithms are fundamentally too different in order to be merged in a natural way. Moreover, due to their rigidity, they are unable to adapt themselves when a non-previously programmed problem appears as it could be the case in our framework. Indeed vision is an auto-adaptive phenomenon which can deal with singular situations by providing particular and adapted treatments. It is also a complex information processing. Therefore, vision should not be reduced to reductionist and simplifying representation. According to this thesis, a vision system could be developed as a whole in which each part adapts itself with others. Its parts cannot be considered separately due to the extreme tensions generated by the complexity and the intricacy of information. Each of them contributes locally to the vision and it is directed by a global objective incomprehensible at its level. We consider vision as a system whose agents deliberate according to an interference produced by the decision potential of each agent. This deliberation is undertaken as the result produced by interferences of a solution superposition. Then, it emerges from the agent-based system a common decision which directs local actions of each agent or of each part of the system. After describing the main shape descriptors and segmentation algorithms and after introducing multi-agent systems on the image processing domain, we discuss on approaches for which vision is considered as a multi-agent system able to manage the inherent complexity of visual information. Then, we give two multi-agent models. The first one deals with an adaptive segmentation which doesn't need manual calibration through thresholds. The second one deals with shape representations through the search of pertinent wavelet coefficients. These two models respect classical image processing criteria. They also are case studies that should be developed in the search of an auto-adaptive vision system

Vers un système de vision auto-adaptatif à base de systèmes multi-agents

Although several image processing approaches exist, each of them was introduced in order to be used in a specific set of applications. In fact, image processing algorithms are fundamentally too different in order to be merged in a natural way. Moreover, due to their rigidity, they are unable to adapt themselves when a non-previously programmed problem appears as it could be the case in our framework. Indeed, vision is an auto-adaptive phenomenon which can deal with singular situations by providing particular and adapted treatments. It is also a complex information processing. Therefore, vision should not be reduced to reductionist and simplifying representation. According to this thesis, a vision system could be developed as a whole in which each part adapts itself with others. Its parts cannot be considered separately due to the extreme tensions generated by the complexity and the intricacy of information. Each of them contributes locally to the vision and it is directed by a global objective incomprehensible at its level. We consider vision as a system which agents deliberate according to an interference produced by the decision potential of each agent. This deliberation is undertaken as the result produced by interferences of a solution superposition. Then, it emerges from the agent-based system a common decision which directs local actions of each agent or of each part of the system. After describing the main shape descriptors and segmentation algorithms and after introducing multi-agent systems on the image processing domain, we discuss on approaches for which vision is considered as a multi-agent system able to manage the inherent complexity of visual information. Then, we give two multi-agent models. The first one deals with an adaptive segmentation which doesn't need manual calibration through thresholds. The second one deals with shape representations through the search of pertinent wavelet coefficients. These two models respect classical image processing criteria. They also are case studies that should be developed in the search of an auto-adaptive vision system.Il existe une multitude de traitements d'images dans la littérature, chacun étant adapté à un ensemble plus ou moins grand de cadres d'application. La généralisation ou la mise en collaboration de ces traitements pour un système plus complet et plus robuste est un problème mal posé. Les traitements d'images sont fondamentalement trop différents les uns par rapport aux autres pour être mis en commun de façon naturelle. De plus, ces derniers sont trop rigides pour pouvoir s'adapter d'eux-mêmes lorsqu'un problème non prévu à l'avance par le concepteur apparaît. Or la vision est un phénomène autoadaptatif, qui sait traiter en temps réel des situations singulières, en y proposant des traitements particuliers et adaptés. Elle est aussi un traitement complexe des informations, tant ces dernières ne peuvent être réduites à des représentations réductionnistes et simplifiantes sans être mutilées. Dans cette thèse, un système de vision est entrepris comme un tout où chaque partie est adaptée à l'autre, mais aussi où chaque partie ne peut s'envisager sans l'autre dans les tensions les plus extrêmes générées par la complexité et l'intrication des informations. Puisque chaque parcelle d'information joue un rôle local dans la vision, tout en étant dirigée par un objectif global peu assimilable à son niveau, nous envisageons la vision comme un système où chaque agent délibère selon une interférence produite par le potentiel décisionnel de chacun de ses voisins. Cette délibération est entreprise comme le résultat produit par l'interférence d'une superposition de solutions. De cette manière, il émerge du système à base d'agents une décision commune qui dirige les actions locales faites par chaque agent ou chaque partie du système. En commençant par décrire les principales méthodes de segmentation ainsi que les descripteurs de formes, puis en introduisant les systèmes multi-agents dans le domaine de l'image, nous discutons d'une telle approche où la vision est envisagée comme un système multi-agent apte à gérer la complexité inhérente de l'information visuelle tant en représentation qu'en dynamisme systémique. Nous ancrons dans ces perspectives deux modèles multi-agents. Le premier modèle traite de la segmentation adaptative d'images sans calibration manuelle par des seuils. Le deuxième modèle traite de la représentation de formes quelconques à travers la recherche de coefficients d'ondelettes pertinents. Ces deux modèles remplissent des critères classiques liés au traitement d'images, et à la reconnaissance de formes, tout en étant des cas d'études à développer pour la recherche d'un système de vision auto-adaptatif tel que nous le décrivons

Planning and Optimization of Resources Deployment: Application to Crisis Management

International audienceCrisis management challenges decision support systems designers. One problem in the decision marking is to develop systems able to help the coordination of the different involved teams. Another challenge is to make the system work with a degraded communication infrastructure. Each workstation or embedded application must be able to help to make a decision with a degraded network by taking into account the potential decisions made by other agents. We propose in this article a multi-agent model, based on an ant colony optimization, and designed to manage the complexity in the deployment of resources to solve a crisis. This model is able to manage data uncertainty, and its global goal is to optimize, in a stable way, fitness functions, like saving lives, defined by multiple users. Moreover, thanks to a reflexive process, the model is able to manage the effects into the environment of its decisions, in order to take more appropriate decisions. Thanks to our transactional model, the system is also able to take into account a large data amount without exploring all potential solutions. The graphical interface should be able to make the user defining rules database. Then, if the nature of the crisis is deeply unchanged, users should be able to change rules' databases

Vers une coordination d'équipes de secours lors de situations de crise : un algorithme ACO prometteur

National audienceRésumé. La gestion de crise pose plusieurs défis aux concepteurs de systèmes d'aide à la décision. Nous proposons dans cet article de traiter la question de la conception de systèmes aptes à orienter intelligemment la coordination des équipes de secours. Un des défis est de permettre au système de fonctionner avec des moyens de communication dégradés. Nous avons développé un modèle d'optimisation par colonies de fourmis, capable de gérer la complexité inhérente dans le déploiement des ressources humaines et matérielles, en tenant compte de l'incertitude des événements présents et futurs d'une crise, ainsi que des effets d'une décision sur le déroulement de la crise, à travers un processus réflexif

A multi-agent approach for the edge detection in image processings

International audienceSeveral multi-agent approaches have been proposed to improve image processing. They use several image processing algorithms simultaneously. However, these approaches do not deal with the inherent problems encountered for the extraction from an image of primitive information like edges or regions. This implies that agents use macro results provided by image processing algorithms. Agents use macro results provided by image processing algorithms. Then, the results do not take advantage of all the interesting characteristics, such as environmental adaptability and emergent behavior capability, of agent-based systems: the combinative explosion of the possible solutions offered by this kind of systems, is highly reduced. In this paper, we propose a multi-agent system based on instinctual [5] reactive agents, which are able to detect edges. Agents locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using a Kirsch derivative and a Gradient Vector Flow. Edges detection emerges from agents interaction. Problems of partial or hidden contours are solved with the cooperation between the different agents. In the scope of this paper, we illustrate our approach through an example that shows how it can be used to detect lungs on 2D images coming from a scan device

Using MAS to Detect Retinal Blood Vessels

International audienc

Retinal blood vessel segmentation by a MAS approach

Retinal blood vessels segmentation by color fundus images analysis has got huge importance for the diabetic retinopathy early diagnosis. Several interesting computational approaches have been done in this field, but none of them has shown the required performance due to the use of global approaches. Therefore, a new approach is proposed based on an organization of agents enabling vessels detection. This multi-agent approach is preceded by a preprocessing phase in which the fundamental filter is a Kirsch derivative improved version. This first phase allows an environment construction where the agents are situated and interact. Then, blood vessels segmentation emerges from agents’ interaction. According to this study, competitive results were achieved comparing to those found in the present literature. It seems to be that a very efficient system for the diabetic retinopathy diagnosis can be built using MAS mechanisms.Fundação para a Ciência e a Tecnologia (FCT

Blood vessel detection in fundus images by a multi-agent approach

The segmentation of retinal blood vessels by digital color fundus images analysis is crucial for several medical diagnostic systems, such as the diabetic retinopathy early diagnosis. This pathology has been shown to be the most common cause of blindness among working age people in developed countries. Several interesting approaches have been done in segmenting the blood vessels by image processing techniques applied in fundus images, but none of them has shown the required performance to be applied in clinical practices. Therefore, a new approach is proposed based on an agents’ organization enabling vessels detection. This multi-agent approach is preceded by a preprocessing phase in which the fundamental filter is a Kirsch derivative improved version. This first phase allows an environment construction where agents are situated and interact. Then, blood vessel edges detection emerged from agent interaction. According to this study, competitive results as compared with those present in the literature were achieved. It seems to be that a very efficient system for diabetic retinopathy diagnosis can be built using MAS mechanisms.Fundação para a Ciência e a Tecnologia (FCT

Using a multi-agent system approach for microaneurysm detection in fundus images

Author's personal copyObjective Microaneurysms represent the first sign of diabetic retinopathy, and their detection is fundamental for the prevention of vision impairment. Despite several research attempts to develop an automated system to detect microaneurysms in fundus images, none has shown the level of performance required for clinical practice. We propose a new approach, based on a multi-agent system model, for microaneurysm segmentation. Methods and materials A multi-agent based approach, preceded by a preprocessing phase to allow construction of the environment in which agents are situated and interact, is presented. The proposed method is applied to two available online datasets and results are compared to other previously described approaches. Results Microaneurysm segmentation emerges from agent interaction. The final score of the proposed approach was 0.240 in the Retinopathy Online Challenge. Conclusions We achieved competitive results, primarily in detecting microaneurysms close to vessels, compared to more conventional algorithms. Despite these results not being optimum, they are encouraging and reveal that some improvements may be made.Fundação para a Ciência e a Tecnologia (FCT