Texture Feature Analysis in Images Using Phase Congruency Based on Log-Gabor Wavelets

Texture feature analysis in images using phase congruency based on Log-Gabor wavelets is a method specifically designed for analyzing texture characteristics in digital images. This technique leverages Log-Gabor wavelets to perform phase congruency analysis, thereby extracting robust texture features from the input image. Phase congruency refers to the phenomenon where different regions of an image share similar phase information in their texture patterns. By examining phase congruency, we gain deeper insights into the texture properties present in the image, making this approach applicable to various domains such as image processing and computer vision. The method holds significant application value in texture feature analysis and introduces a novel perspective for related research. From an implementation standpoint, the process typically involves constructing a bank of Log-Gabor filters at multiple scales and orientations, computing the local phase and magnitude responses, and then applying phase congruency measures to identify perceptually significant texture features. Key functions in MATLAB or similar environments would include designing Log-Gabor filters, performing multi-scale convolution, and calculating phase congruency maps to highlight texture regions with high consistency.