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Proceedings Paper

Multiscale image segmentation using joint texture and shape analysis
Author(s): Ramesh Neelamani; Justin K. Romberg; Hyeokho Choi; Rudolf H. Riedi; Richard G. Baraniuk
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Paper Abstract

We develop a general framework to simultaneously exploit texture and shape characterization in multiscale image segmentation. By posing multiscale segmentation as a model selection problem, we invoke the powerful framework offered by minimum description length (MDL). This framework dictates that multiscale segmentation comprises multiscale texture characterization and multiscale shape coding. Analysis of current multiscale maximum a posteriori segmentation algorithms reveals that these algorithms implicitly use a shape coder with the aim to estimate the optimal MDL solution, but find only an approximate solution.

Paper Details

Date Published: 4 December 2000
PDF: 14 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408607
Show Author Affiliations
Ramesh Neelamani, Rice Univ. (United States)
Justin K. Romberg, Rice Univ. (United States)
Hyeokho Choi, Rice Univ. (United States)
Rudolf H. Riedi, Rice Univ. (United States)
Richard G. Baraniuk, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 4119:
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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