Image Texture Recognition Using Gabor Transform

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Image texture recognition based on Gabor transform with algorithmic enhancements. This study presents an improved Gabor transform algorithm for texture enhancement, converting spatial domain texture images to joint spatial-frequency domain and utilizing energy distribution as palmprint features. The implementation involves Gabor filtering of frequency energy distribution along principal directions in sub-blocks, feature vector matching, multi-directional spatial-frequency energy filtering synthesis for enhanced ridge information, and computational optimization for reduced processing load.

Detailed Documentation

This article discusses image texture recognition using Gabor transform. We propose an enhanced Gabor transform algorithm that improves texture detail enhancement through computational implementation. The algorithm transforms texture images from spatial domain to joint spatial-frequency domain, where energy distribution serves as palmprint features. Our implementation applies Gabor filters to frequency energy distributions along principal directions within sub-blocks, utilizing feature vectors for matching recognition. Code implementation includes multi-directional spatial-frequency energy filtering synthesis to enhance characteristic ridge information, with optimized core region processing. The optimized filtering algorithm significantly reduces computational complexity through efficient frequency domain operations and parallel processing techniques.

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