Image Preprocessing and Feature Extraction Techniques

This article discusses various image preprocessing techniques including grayscale enhancement for contrast improvement, wavelet-based high-pass filtering for edge detection, Local Binary Patterns (LBP) for texture analysis, iterative threshold segmentation for automatic binarization, and region growing algorithms for connected component extraction. The implementation typically involves using Python's OpenCV and scikit-image libraries, where grayscale enhancement can be achieved through histogram equalization (cv2.equalizeHist()), wavelet filtering using pywt package, LBP implementation via skimage.feature.local_binary_pattern, and iterative thresholding using Otsu's method. Additionally, the content covers feature extraction methodologies for RGB color spaces using color moment calculations and texture features through gray-level co-occurrence matrix (GLCM) implementation with skimage.feature.graycomatrix for statistical texture analysis.