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Optimal spatial filter selection for illumination-invariant color texture discrimination.

B Thai¹, G Healey

¹Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 7, 2008

Summary

Researchers developed optimal spatial filters for distinguishing color textures under varying illumination. This method enhances recognition by maximizing differences between texture features, improving discrimination accuracy.

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Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Color textures possess rich spectral and spatial information crucial for recognition.
Spatial filters can extract illumination-invariant spatial information from color images.

Purpose of the Study:

To derive optimal spatial filters for illumination-invariant color texture discrimination.
To develop a method for maximizing texture discriminability among multiple classes.

Main Methods:

Representing color textures using illumination-invariant features derived from filtered image regions.
Deriving a spatial filter that maximizes feature space distance between pairs of color textures.
Extending pairwise optimization to multi-class discrimination.

Main Results:

Demonstrated improved discriminatory power using optimized filters.
Validated performance on deterministic and random color textures under diverse illumination.
Showcased the effectiveness of derived features for texture characterization.

Conclusions:

Optimized spatial filters significantly enhance illumination-invariant color texture discrimination.
The proposed method offers a robust approach for texture recognition across varying lighting conditions.
This work contributes to advancing the field of computer vision for texture analysis.