An object detection and recognition system for weld bead extraction from digital radiographs
Computer Vision and Image Understanding
A Framework for Adaptive and Integrated Classification
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
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Computer Methods and Programs in Biomedicine
An object detection and recognition system for weld bead extraction from digital radiographs
Computer Vision and Image Understanding
A new supervised evaluation criterion for region based segmentation methods
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Boolean derivatives with application to edge detection for imaging systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Face authentication using the trace transform
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Software agent with reinforcement learning approach for medical image segmentation
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EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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Applied Soft Computing
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The paper presents a general approach to image segmentation and object recognition that can adapt the image segmentation algorithm parameters to the changing environmental conditions. Segmentation parameters are represented by a team of generalized stochastic learning automata and learned using connectionist reinforcement learning techniques. The edge-border coincidence measure is first used as reinforcement for segmentation evaluation to reduce computational expenses associated with model matching during the early stage of adaptation. This measure alone, however, cannot reliably predict the outcome of object recognition. Therefore, it is used in conjunction with model matching where the matching confidence is used as a reinforcement signal to provide optimal segmentation evaluation in a closed-loop object recognition system. The adaptation alternates between global and local segmentation processes in order to achieve optimal recognition performance. Results are presented for both indoor and outdoor color images where the performance improvement over time is shown for both image segmentation and object recognition