Image Texture Analysis Based on Wavelet Transform and Steerable Pyramid Algorithm

Image texture analysis based on wavelet transform and steerable pyramid algorithm is a method for studying and describing texture features within images. Wavelet transform serves as a mathematical tool that decomposes signals or images into sub-bands of different frequencies, typically implemented using filter banks like Daubechies or Haar wavelets through discrete wavelet transform (DWT) operations. The steerable pyramid algorithm is an image processing technique that constructs multi-scale, multi-orientation representations using pyramid decomposition, enabling directional filtering through basis function rotations. By integrating both methods, texture features can be analyzed more accurately through: 1) Multi-resolution decomposition via wavelet filters 2) Orientation-selective filtering using steerable basis functions 3) Joint statistical analysis of coefficients across scales. Key implementation steps involve applying DWT for frequency localization, building steerable pyramids with directional filters, and extracting texture descriptors like energy or entropy from sub-bands. This approach finds extensive applications in image processing and computer vision domains, including texture classification, segmentation, and pattern recognition tasks.