Image Feature Extraction Using Gabor Wavelets

In this documentation, we employ Gabor wavelets for image feature extraction. For those unfamiliar with Gabor wavelets, they represent a specialized wavelet type commonly used in signal processing that decomposes signals into distinct frequency and orientation components. The key advantage of Gabor wavelets lies in their ability to capture features across multiple orientations and frequencies, making them particularly valuable for image processing applications. The implementation typically involves constructing Gabor filters with specific parameters (wavelength, orientation, phase offset, and bandwidth) and convolving them with input images to generate feature maps. Our provided code includes functions for creating Gabor filter banks, handling multi-scale and multi-orientation processing, and extracting texture features through response aggregation. The complete implementation is production-ready and includes parameter optimization guidelines for different image types. Please contact us if you require additional technical specifications or implementation details.