Toward self-adaptive vision system based on multi-agents systems.
type: Dataset
description: 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 c…
issued: 2026-05-07T05:39:02.088998
language: fr
modified: 2026-05-07T05:39:02.089002
contact point: Organization
  fn: CCSD
keyword: artificial-vision
keyword: auto-adaptive-systems
keyword: complex-systems
keyword: image-processing
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-aicomputer-science-csartificial-intelligence-csai
keyword: infoinfo-cccomputer-science-cscomputational-complexity-cscc
keyword: infoinfo-cvcomputer-science-cscomputer-vision-and-pattern-recognition-cscv
keyword: infoinfo-imcomputer-science-csmedical-imaging
keyword: infoinfo-macomputer-science-csmultiagent-systems-csma
keyword: infoinfo-ticomputer-science-csimage-processing-eessiv
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: multi-agent-systems
keyword: pattern-recognition
keyword: reconnaissance-de-formes
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: systemes-auto-adaptatifs
keyword: systemes-complexes
keyword: systemes-mutli-agents
keyword: theses
keyword: traitement-dimages
keyword: vision-artificielle
landing page: https://theses.hal.science/tel-00937422

Toward self-adaptive vision system based on multi-agents systems.
type: Distribution
format: HTML
issued: 2026-05-07T05:39:02.090939
modified: 2026-05-07T05:39:02.068004
access URL: https://theses.hal.science/tel-00937422

test_moissonnage_selune
type: Agent