Boundary estimation from intensity/color images with algebraic curve models

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Publication Type Journal Article
School or College College of Engineering
Department Electrical & Computer Engineering
Creator Tasdizen, Tolga
Other Author Cooper, David B.
Title Boundary estimation from intensity/color images with algebraic curve models
Date 2000
Description A new concept and algorithm are presented for noniterative robust estimation of piecewise smooth curves of maximal edge strength in small image windows - typically 8 x 8 to 32 x 32. This boundary-estimation algorithm has the nice properties that it uses all the data in the window and thus can find locally weak boundaries embedded in noise or texture and boundaries when there are more than two regions to be segmented in a window; it does not require step edges - but handles ramp edges well. The curve-estimates found are among the level sets of a d'th degree polynomial fit to "suitable" weightings of the image gradient vector at each pixel in the image window. Since the polynomial fitting is linear least squares, the computation to this point is very fast. Level sets then chosen to be appropriate boundary curves are those having the highest differences in average gray level in regions to either side. This computation is also fast. The boundary curves and segmented regions found are suitable for all purposes but especially for indexing using algebraic curve invariants in this form.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 225
Last Page 228
Language eng
Bibliographic Citation Tasdizen, T., & Cooper, D. B., (2000). Boundary estimation from intensity/color images with algebraic curve models. Proceedings of 15th IEEE Computer Society International Conference on Pattern Recognition (ICPR), 1, 225-8.
Rights Management (c) 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 502,336 bytes
Identifier ir-main,15237
ARK ark:/87278/s6v7036m
Setname ir_uspace
Date Created 2012-06-13
Date Modified 2021-05-06
ID 706872
Reference URL https://collections.lib.utah.edu/ark:/87278/s6v7036m
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