Gabor Filter for Image Texture Extraction and Edge Detection

The Gabor filter is a widely used filter in image processing, primarily employed for extracting texture information and performing edge detection. Its fundamental principle involves applying filtering operations to images to enhance texture details and edge characteristics. The Gabor filter operates by convolving the input image with a set of Gabor kernels - complex sinusoidal functions modulated by Gaussian envelopes. These kernels can be tuned to specific frequencies and orientations using parameters like wavelength (lambda), orientation (theta), and bandwidth. Typical implementation involves generating Gabor kernels through mathematical functions that combine Gaussian distributions with complex sinusoids. Key parameters include: - Kernel size (kernel_size): Determines the spatial extent of the filter - Sigma (σ): Controls the Gaussian envelope's standard deviation - Theta (θ): Sets the orientation angle of the filter - Lambda (λ): Specifies the wavelength of the sinusoidal component - Gamma (γ): Defines the spatial aspect ratio - Psi (ψ): Represents the phase offset In practical applications, Gabor filters are often applied in multiple orientations (typically 0°, 45°, 90°, 135°) to capture texture features in different directions. The filter response can be computed using either the real part (even-symmetric) for edge detection or the magnitude for texture analysis. For edge detection, the real component responds strongly to edge-like structures, while for texture extraction, the filter magnitude provides rotation-invariant texture features. Using Gabor filters enables better understanding of image texture structures, facilitating more precise image analysis and processing. The technique is particularly effective in applications like fingerprint recognition, medical image analysis, and texture classification where multi-scale and multi-orientation feature extraction is crucial.