A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation labelling algorithms-a review
Image and Vision Computing
On compatibility and support functions in probabilistic relaxation
Computer Vision, Graphics, and Image Processing
Edge detection and motion detection
Image and Vision Computing
A Parallel Architecture for Discrete Relaxation Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-Labeling Using Dictionary-Based Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Ground from Figure Discrimination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A new fuzzy relaxation algorithm for image enhancement
International Journal of Knowledge-based and Intelligent Engineering Systems
An edge detection method by combining fuzzy logic and neural network
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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This paper presents a new scheme for probabilistic relaxation labeling that consists of an update function and a dictionary construction method. The nonlinear update function is derived from Markov Random Field theory and Bayes' formula. The method combines evidence from neighboring label assignments and eliminates label ambiguity efficiently. This result is important for a variety of image processing tasks, such as image restoration, edge enhancement, edge detection, pixel classification, and image segmentation.We successfully applied this method to edge detection. The relaxation step of the proposed edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial role in refining edge outputs. The experiments show that our algorithm converges quickly and is robust in noisy environments.