Multiscale image segmentation using joint texture and shape analysis

Ramesh Neelamani; Justin K. Romberg; Hyeokho Choi; Rudolf H. Riedi; Richard G. Baraniuk

doi:10.1117/12.408607

4 December 2000 Multiscale image segmentation using joint texture and shape analysis

Ramesh Neelamani, Justin K. Romberg, Hyeokho Choi, Rudolf H. Riedi, Richard G. Baraniuk

Author Affiliations +

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408607
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

Citation Download Citation

Ramesh Neelamani, Justin K. Romberg, Hyeokho Choi, Rudolf H. Riedi, and Richard G. Baraniuk "Multiscale image segmentation using joint texture and shape analysis", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408607

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