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Content Provider | IEEE Xplore Digital Library |
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Author | Malik, J. Perona, P. |
Copyright Year | 1989 |
Description | Author affiliation: Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA (Malik, J.; Perona, P.) |
Abstract | An algorithm for finding texture boundaries in images is developed on the basis of a computational model of human texture perception. The model consists of three stages: (1) the image is convolved with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses; (2) inhibition, localized in space, within and among the neural response profiles results in the suppression of weak responses when there are strong responses at the same or nearby locations; and (3) texture boundaries are detected using peaks in the gradients of the inhibited response profiles. The model is precisely specified, equally applicable to grey-scale and binary textures, and is motivated by detailed comparison with psychophysics and physiology. It makes predictions about the degree of discriminability of different texture pairs which match very well with experimental measurements of discriminability in human observers. From a machine-vision point of view, the scheme is a high-quality texture-edge detector which works equally on images of artificial and natural scenes. The algorithm makes the use of simple local and parallel operations, which makes it potentially real-time.< |
Starting Page | 326 |
Ending Page | 332 |
File Size | 891004 |
Page Count | 7 |
File Format | |
ISBN | 081861952X |
ISSN | 10636919 |
DOI | 10.1109/CVPR.1989.37868 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1989-06-04 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Computational modeling Image edge detection Humans Machine vision Brightness Testing Parameter estimation Image segmentation Electronic mail Nonlinear filters |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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