Image Segmentation Using Graph-Based Technique in Computer Vision
This project aims to develop a program that implements a graph-based image segmentation algorithm. The goal is to partition an image into meaningful regions by leveraging graph theory, where each pixel represents a node and edges between nodes represent the similarity or dissimilarity between pixels based on intensity differences. This will be achieved by creating and processing edges between adjacent pixels, sorting them by weight, and merging segments based on a defined threshold. The implementation will be evaluated for its accuracy, efficiency, and robustness in handling various types of images. Ultimately, the project aims to enhance image analysis and processing by visually representing the segmented regions and contributing to advancements in computer vision and image processing applications.