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Circular-Mellin features for texture segmentation.

G Ravichandran¹, M M Trivedi

¹Dept. of Electr. and Comput. Eng., Tennessee Univ., Knoxville, TN.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1995

Summary

We developed novel circular-Mellin operators for distortion-invariant texture segmentation. These operators achieve rotation and scale invariance, outperforming Gabor functions for image analysis.

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Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Texture analysis is crucial for image segmentation.
Existing methods like Gabor filters have limitations in handling scale and rotation variations.

Purpose of the Study:

To introduce and analyze novel circular-Mellin operators.
To demonstrate their effectiveness in distortion-invariant texture segmentation.

Main Methods:

Development of circular-Mellin operators based on spectral decomposition in a polar-log coordinate system.
Utilizing correlator architecture for shift invariance.
Feature extraction for rotation and scale invariance.

Main Results:

Circular-Mellin operators exhibit invariance to scale and orientation.
These operators provide superior texture segmentation compared to Gabor functions.
Demonstrated effective feature extraction capabilities.

Conclusions:

Circular-Mellin operators offer a robust solution for distortion-invariant texture segmentation.
Their unique properties make them highly suitable for complex image analysis tasks.