Image Processing I: Segmentation and Thresholding

This lecture covers the concepts of image segmentation and thresholding in image processing. It starts with an overview of filterbank analysis and DCT filters, leading to the discussion of segmentation techniques such as amplitude thresholding, variational thresholding, and statistical thresholding. The lecture delves into the challenges of image segmentation, emphasizing the importance of homogeneity and connectivity criteria. It explores the principles behind variational thresholding and statistical thresholding, highlighting the process of finding the most likely segmentation model. Additionally, the lecture addresses texture segmentation by vector quantization and binary-segmentation techniques, including distance measures and connectivity considerations. The session concludes with a detailed explanation of connected-component labeling and blob coloring algorithms for image analysis.