Gabor Wavelet Feature Extraction
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In this text, we will use Gabor wavelets to extract features from an input image. Gabor wavelet transform is a feature extraction method based on Gabor wavelets that helps capture important characteristics such as texture patterns and edge information in images. By applying Gabor wavelet transform to the input image, typically implemented through a bank of Gabor filters with varying scales and orientations using functions like MATLAB's "imgaborfilt" or OpenCV's "getGaborKernel", we obtain a set of feature representations rich in information. These features can be utilized in computer vision tasks such as image classification, object detection, and image recognition. Therefore, feature extraction using Gabor wavelets serves as an extremely effective approach for analyzing texture-dominated images, with implementation involving convolution operations between the image and complex Gabor kernels across multiple frequencies and directions.
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