Adaptive Agent Based System for Knowledge Discovery in Visual Information
msra
摘要
Abstract One of the main limitations of most current machine vision systems is a lack of flexibility toconsider,the wide ,variety of information ,provided ,by visual ,data. The research proposed here aims to improve ,this situation by the ,development ,of an ,adaptive visual system able to selectively combine,information from different visual algorithms. The problem is cast as a knowledge discovery problem, where the two main steps are detection and characterization of ,relevant patterns. The algorithms will be able ,to perceive different attributes of the visual space such as color, depth, motion or specific shapes. The intended system ,should ,be able ,to adaptively ,select and combine ,the information provided by the algorithms according to the quality of the information given by each of them. The system proposed is based on an intelligent agent,paradigm. Each visual module,will beimplemented,as an ,agent that will be able ,to adapt ,its behavior according to the relevant task and environment ,constraints. The adaptation will be provided ,by a ,local self-evaluation function on each,agent. Cooperation among,the agents will be given,by a probabilistic scheme,that will integrate the evidential information provided by them. The proposed ,system aims to achieve ,two highly desirable attributes of an ,engineering system: robustness and,efficiency. By combining,the outputs of multiple vision modules the assumptions ,and constrains of each ,module ,will be factored ,out to result in a more robust system ,overall. Efficiency will be still kept through the on-line selection and specialization of the ,algorithms according ,to the ,relevant structures and conditions
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