MATLAB Image Processing Implementation for Feature Detection and Segmentation

This document delves into key concepts of MATLAB image processing, covering point detection, line detection, edge detection, edge linking, and thresholding techniques. MATLAB serves as a powerful platform for processing and analyzing diverse image types through its built-in functions and customizable algorithms. Point detection algorithms, often implemented using high-pass filters like the Laplacian operator (via fspecial('laplacian')), isolate discrete points by enhancing intensity variations. Line detection leverages directional filters (e.g., Prewitt or Sobel kernels) or Hough transform (hough function) to identify linear structures with precise orientation and position data. Edge detection—a critical preprocessing step—highlights boundaries between regions using gradient-based methods (edge function with Canny or Sobel detectors). Edge linking algorithms, such as morphological operations (imclose) or edge-following techniques, connect discontinuous edges to form coherent boundaries. Thresholding, implemented via imbinarize or Otsu's method (graythresh), segments images into distinct regions based on pixel intensity histograms. These techniques form the foundation of MATLAB image processing workflows, combining theoretical principles with practical code implementations for robust image analysis.