MATLAB Implementation of Multiple Image Edge Detection and Segmentation Techniques

This project implements multiple image edge detection and segmentation algorithms using MATLAB to enhance the accuracy and effectiveness of image processing. Through sequential stages of image preprocessing, edge detection, and segmentation processing, the system can extract regions of interest from images for further analysis and applications. Key implementation approaches include: - Utilizing MATLAB's Image Processing Toolbox functions for noise reduction and contrast enhancement during preprocessing - Implementing Canny edge detection algorithm with adjustable threshold parameters for optimal edge localization - Applying region growing algorithms with customizable seed point selection and similarity criteria The system supports various image processing techniques including: 1. Canny edge detection using edge() function with Gaussian filter optimization 2. Region-based segmentation through watershed algorithms and morphological operations 3. Threshold-based segmentation methods using graythresh() and imbinarize() functions These edge detection and segmentation techniques provide valuable foundations for image recognition, computer vision applications, and research directions in digital image analysis. The modular MATLAB implementation allows for easy parameter adjustment and comparison between different algorithmic approaches.