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A network for multiscale image segmentation

Perona, Pietro and Malik, Jitendra (1988) A network for multiscale image segmentation. In: IEEE International Symposium on Circuits and Systems; Proceedings. No.3. IEEE , Piscataway, NJ, pp. 2565-2568. ISBN 9517212410.

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Detecting edges of objects in their images is a basic problem in computational vision. The scale-space technique introduced by Witkin [11] provides means of using local and global reasoning in locating edges. This approach has a major drawback: it is difficult to obtain accurately the locations of the 'semantically meaningful' edges. We have refined the definition of scale-space, and introduced a class of algorithms for implementing it based on using anisotropic diffusion [9]. The algorithms involves simple, local operations replicated over the image making parallel hardware implementation feasible. In this paper we present the major ideas behind the use of scale space, and anisotropic diffusion for edge detection, we show that anisotropic diffusion can enhance edges, we suggest a network implementation of anisotropic diffusion, and provide design criteria for obtaining networks performing scale space, and edge detection. The results of a software implementation are shown.

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© 1988 IEEE. Date of Current Version: 06 August 2002. This work has been supported by grant 82-11-008 of the Semiconductor Research Corporation , and an IBM faculty development award.
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Semiconductor Research Corporation82-11-008
IBM Faculty Development AwardUNSPECIFIED
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INSPEC Accession Number3339973
Issue or Number:3
Record Number:CaltechAUTHORS:20120621-094432886
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Official Citation:Perona, P.; Malik, J.; , "A network for multiscale image segmentation," Circuits and Systems, 1988., IEEE International Symposium on , vol., no., pp.2565-2568 vol.3, 7-9 Jun 1988 doi: 10.1109/ISCAS.1988.15465 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:32000
Deposited By: Ruth Sustaita
Deposited On:21 Jun 2012 18:05
Last Modified:09 Nov 2021 20:02

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