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Illusory contour detection using MRF models

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3 Author(s)
S. Madarasmi ; Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA ; Ting-Chuen Pong ; D. Kersten

This paper presents a computational model for obtaining relative depth information from image contours. Local occlusion properties such as T-junctions and concavity are used to arrive at a global percept of distinct surfaces at various relative depths. A multilayer representation is used to classify each image pixel into the appropriate depth plane based on the local information from the occluding contours. A Bayesian framework is used to incorporate the constraints defined by the contours and the prior constraints. A solution corresponding to the maximum posteriori probability is then determined, resulting in a depth assignment and surface assignment for each image site or pixel. The algorithm was tested on various contour images, including two classes of illusory surfaces: the Kanizsa (1979) and the line termination illusory contours

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994