Exploring Cutting-Edge Deep Learning Techniques in Image Processing

This research delves into advanced image processing techniques for computer vision applications. A wide range of feature extraction methods, including LBP, LTP, RGB channel splitting, histogram equalization, grayscale conversion, and resizing are also explored. K-Mean Clustering (K=2) aids in image segmentation. Color transformations like negation, log, and gamma correction are employed. Color spaces (HSV, LAB, YCrCb) are explored. Shape analysis is conducted via GLCM and Haralick Texture analysis. Image sharpening involves Prewitt, Scharr, Sobel filters, and frequency domain analysis using Gaussian filters. This work contributes to enhanced image analysis for various applications.