DocumentCode
1682550
Title
Invariant noisy texture classification with bispectrum-based features from projections
Author
Horikawa, Yo
Author_Institution
Fac. of Eng., Kagawa Univ., Takamatsu, Japan
Volume
2
fYear
2001
Firstpage
606
Abstract
The author presents a novel calculation method of invariant features based on the bispectrum from the projections of images. The invariant feature is applied to the classification of sinusoidal patterns and natural texture images suffering from similarity transformations and noise. High classification performance is obtained under arbitrary rotation, small shift and scaling, as well as considerable additive noise
Keywords
feature extraction; image classification; noise; spectral analysis; additive noise; bispectrum; bispectrum-based features; classification performance; image projections; invariant features; invariant noisy texture classification; natural texture images; pattern recognition; rotation; scaling; similarity transformations; sinusoidal patterns classification; small shift; Additive noise; Computational complexity; Fourier transforms; Frequency; Gaussian noise; Image processing; Interpolation; Noise robustness; Pattern recognition; Phase noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location
Thessaloniki
Print_ISBN
0-7803-6725-1
Type
conf
DOI
10.1109/ICIP.2001.958566
Filename
958566
Link To Document