DocumentCode
3544992
Title
Texture segmentation using multiscale Hurst features
Author
Kaplan, Lance M. ; Murenzi, Romain
Author_Institution
Dept. of Eng., Clark Atlanta Univ., GA, USA
Volume
3
fYear
1997
fDate
26-29 Oct 1997
Firstpage
205
Abstract
We evaluate the effectiveness of multiscale Hurst parameters as features for texture segmentation. These extended Hurst features quantize texture roughness at various scales. The performance of these new features are compared against standard Hurst features using images of texture mosaics. For the experiments, the performance was evaluated with and without supplemental contrast and average grayscale features
Keywords
feature extraction; image segmentation; image texture; parameter estimation; quantisation (signal); average grayscale features; contrast; experiments; extended Hurst features; extended self-similarity; image texture; multiscale Hurst features; multiscale Hurst parameters; performance evaluation; standard Hurst features; texture mosaics; texture roughness quantization; texture segmentation; Digital images; Electronic switching systems; Fractals; Humans; Image motion analysis; Image segmentation; Image texture analysis; Layout; Remote monitoring; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 1997. Proceedings., International Conference on
Conference_Location
Santa Barbara, CA
Print_ISBN
0-8186-8183-7
Type
conf
DOI
10.1109/ICIP.1997.632056
Filename
632056
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