Multispectral image segmentation using a multiscale model

Author

Bouman, Charles ; Shapiro, Michael

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

3

fYear

1992

fDate

23-26 Mar 1992

Firstpage

565

Abstract

A new approach to Bayesian image segmentation based on a novel multiscale random field (MSRF) and a new estimation approach called sequential maximum a posteriori estimation are presented. Together, the proposed estimator and model result in a segmentation algorithm which is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. A method for estimating the parameters of the multiscale model directly from the image during the segmentation process is developed

Keywords

Bayes methods; graph theory; image segmentation; parameter estimation; Bayesian image segmentation; multiscale model; multiscale random field; multispectral image segmentation; parameter estimation; pyramidal graph model; quadtree model; sequential maximum a posteriori estimation; Bayesian methods; Image segmentation; Iterative algorithms; Laboratories; Markov random fields; Military computing; Multispectral imaging; National electric code; Parameter estimation; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226150

Filename

226150