Modified Log Gabor Wavelet Image Texture Feature Analysis

In this paper, I have modified and extended the Log Gabor wavelet image texture feature analysis. This analytical approach enables a more comprehensive understanding of texture characteristics within images, which is crucial for image processing and pattern recognition domains. By implementing Log Gabor wavelets through frequency-domain filtering techniques, we can capture finer details using multi-scale and multi-orientation filters. The algorithm typically involves constructing Log Gabor filter banks in the frequency domain, applying them to image Fourier transforms, and extracting texture features from the filtered responses. This method demonstrates superior performance in describing image texture features compared to traditional Gabor filters, particularly in handling high-frequency components and maintaining frequency sensitivity across scales. These enhancements find broad applications in various fields including image analysis, facial recognition systems, and medical image processing. Therefore, research and improvements in image texture feature analysis hold significant importance. The modifications aim to better demonstrate the significance and practical value of image texture feature analysis in real-world applications.