Image Processing with Gabor Wavelet Filtering
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Resource Overview
Images filtered using Gabor wavelet transformation through convolution operations between Gabor wavelets and input images.
Detailed Documentation
In this study, we employed Gabor wavelet filters for image processing. Gabor wavelets are complex functions based on Gaussian distributions, whose convolution operations can effectively extract edge and texture features from images. The implementation typically involves convolving input images with Gabor wavelet filters at multiple orientations and scales, producing enhanced output images where edge and texture characteristics become more pronounced. This process can be implemented using mathematical convolution operations with Gabor kernels parameterized by wavelength (λ), orientation (θ), phase offset (φ), and bandwidth (γ). The resulting filtered images demonstrate improved quality and more accurate feature extraction capabilities, making Gabor filters particularly valuable for computer vision applications like texture analysis and edge detection.
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