Optimization by simulated annealing
Readings in computer vision: issues, problems, principles, and paradigms
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Image Segmentation With Distributed Behavior-Based Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in range images based on scan line approximation
Computer Vision and Image Understanding
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Markov random field modeled range image segmentation
Pattern Recognition Letters
Range image segmentation based on randomized Hough transform
Pattern Recognition Letters
Automated segmentation of human brain MR images using a multi-agent approach
Artificial Intelligence in Medicine
Range image segmentation using surface selection criterion
IEEE Transactions on Image Processing
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We present in this paper a multi-agent approach for range image segmentation. The approach consists in using autonomous agents for the segmentation of a range image in its different planar regions. Agents move on the image and perform local actions on the pixels, allowing robust region extraction and accurate edge detection. In order to improve the segmentation quality, a Bayesian edge regularization is applied to the resulting edges. A new Markov Random Field (MRF) model is introduced to model the edge smoothness, used as a prior in the edge regularization. The experimental results obtained with real images from the ABW database show a good potential of the proposed approach for range image analysis, regarding both segmentation efficiency, and detection accuracy.