Learning Path

Image Processing Fundamentals → Segmentation Evaluation and Applications

This graduate-level path guides computer vision practitioners through the fundamental and advanced techniques for partitioning images into meaningful regions and objects. Starting with image preprocessing and thresholding, it progresses through region-based, clustering, and edge-based methods, then moves to graph-based and modern deep learning segmentation architectures, including U-Net and Mask R-CNN. The path emphasizes practical application and the mathematical foundations necessary to understand and implement each approach.