MATLAB Gabor Feature Computation Toolbox Developed by International Researchers

This MATLAB Gabor feature computation toolbox, developed by international researchers, provides robust implementations of Gabor filters for both one-dimensional and two-dimensional feature extraction. Gabor filters are widely used in image processing and pattern recognition due to their excellent localization properties in both frequency and orientation domains. The toolbox offers the following key functionalities: One-dimensional Gabor feature extraction: Ideal for time-series signal analysis such as speech processing or sensor data interpretation. The implementation uses efficient convolution operations with complex Gabor kernels, allowing real and imaginary component analysis. Two-dimensional Gabor feature extraction: Designed for image processing applications, supporting multi-scale and multi-orientation feature computation. The algorithm employs optimized 2D convolution with customizable kernel sizes and orientation parameters. Flexible parameter configuration: Users can adjust critical parameters including wavelength, orientation angles, phase offsets, and spatial aspect ratio. The toolbox provides helper functions for parameter validation and kernel generation. Gabor features are extensively applied in texture analysis, facial recognition, and medical image processing. This toolkit enhances computational efficiency through algorithm optimizations such as separable convolution implementations and frequency domain processing where applicable, making it suitable for both research and engineering applications. The code structure follows MATLAB best practices with clear function documentation and example scripts demonstrating typical usage scenarios.