Texture classification and segmentation using simultaneous autoregressive random model

Author

Liao, Mengyang ; Qin, Jiamei ; Tan, Yanni

Author_Institution

Dept. of Radio Inf. Eng., Wuhan Univ., China

fYear

1992

fDate

14-17 Jun 1992

Firstpage

398

Lastpage

401

Abstract

The simultaneous autoregressive (SAR) model is used to describe texture. The authors also propose using the least-squares method to estimate six SAR parameters. Based on the SAR model and the parameter estimation method, experiments have been done to classify and segment images of various natural textures and human B-scan images. Excellent results have been obtained

Keywords

biomedical ultrasonics; image recognition; image segmentation; image texture; least squares approximations; medical image processing; parameter estimation; human B-scan images; least-squares method; parameter estimation; simultaneous autoregressive random model; texture classification; texture segmentation; Gaussian noise; Image classification; Image edge detection; Image segmentation; Liver; Maximum likelihood estimation; Parameter estimation; Pixel; Random variables; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on

Conference_Location

Durham, NC

Print_ISBN

0-8186-2742-5

Type

conf

DOI

10.1109/CBMS.1992.244923

Filename

244923

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3364986