Parameter estimation and hypothesis testing in linear models
Parameter estimation and hypothesis testing in linear models
Elements of information theory
Elements of information theory
Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual organization in computer vision: status, challenges, and potential
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
A probabilistic method for extracting chains of collinear segments
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
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An information theoretic framework for grouping observations is proposed. The entropy change incurred by new observations is analyzed using the Kalman filter update equations. It is found, that the entropy variation is caused by a positive similarity term and a negative proximity term. Bounding the similarity term in the spirit of the minimum description length principle and the proximity term in the spirit of maximum entropy inference a robust and efficient grouping procedure is devised. Some of its properties are demonstrated for the exemplary task of edgel grouping.