DocumentCode :
931581
Title :
Texture boundary detection based on the long correlation model
Author :
Kashyap, Rangasami L. ; Eom, Kie-Bum
Author_Institution :
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
Volume :
11
Issue :
1
fYear :
1989
fDate :
1/1/1989 12:00:00 AM
Firstpage :
58
Lastpage :
67
Abstract :
The problem of detecting texture boundaries without assuming any knowledge on the number of regions or the types of textures is considered. Texture boundaries are often regarded as better features than intensity edges, because a large class of images can be considered a composite of several different texture regions. An algorithm is developed that detects texture boundaries at reasonably high resolution without assuming any prior knowledge on the texture composition of the image. The algorithm utilizes the long correlation texture model with a small number of parameters to characterize textures. The parameters of the model are estimated by a least-squares method in the frequency domain. The existence and the location of texture boundary is estimated by the maximum-likelihood method. The algorithm is applied to several different images, and its performance is shown by examples. Experimental results show that the algorithm successfully detects texture boundaries without knowing the number of types of textures in the image
Keywords :
parameter estimation; pattern recognition; picture processing; frequency domain; least-squares method; long correlation model; maximum-likelihood method; pattern recognition; picture processing; texture boundary detection; Face detection; Frequency domain analysis; Frequency estimation; Humans; Image analysis; Image edge detection; Image resolution; Image segmentation; Layout; Least squares methods;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.23113
Filename :
23113
Link To Document :
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