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Steerable-scalable kernels for edge detection and junction analysis

Perona, Pietro (1992) Steerable-scalable kernels for edge detection and junction analysis. Image and Vision Computing, 10 (10). pp. 663-372. ISSN 0262-8856. doi:10.1016/0262-8856(92)90011-Q.

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Families of kernels that are useful in a variety of early vision algorithms may be obtained by rotating and scaling in a continuum a 'template' kernel. These multiscale multi-orientation families may be approximated by linear interpolation of a discrete finite set of appropriate 'basis' kernels. A scheme for generating such a basis, together with the appropriate interpolation weights, is described. Unlike previous schemes by Perona and Simoncelli et al., it is guaranteed to generate the most parsimonious basic kernel. Additionally, it is shown how to exploit two symmetries in edge-detection kernels for reducing storage and computational costs, and for generating simultaneously endstop- and junction-tuned filters for free.

Item Type:Article
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URLURL TypeDescription ItemConference Paper
Perona, Pietro0000-0002-7583-5809
Additional Information:© 1992 Published by Elsevier B.V. Received 26 May 1992. This work was partially conducted while at MIT-LIDS with the Center for Intelligent Control Systems sponsored by ARO grant DAAL 03-86-K-0171.
Funding AgencyGrant Number
Army Research Office (ARO)DAAL 03-86-K-0171
Subject Keywords:Early Vision, Grouping, Diffusions
Issue or Number:10
Record Number:CaltechAUTHORS:20140805-113943781
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47976
Deposited On:05 Aug 2014 23:13
Last Modified:10 Nov 2021 17:52

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