Chapter 6. Image Segmentation 1. Fundamentals 2. Point, Line, and Edge Detection 3. Thresholding 4. Image Segmentation Using Deep Learning Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 2 1. Fundamentals ➢ Let R represent the entire spatial region occupied by an image. We may view image segmentation as a process that partitions R into n subregions, R1, R2, …, Rn, such that: where Q(Rk) is a logical predicate defined over the points in set Rk and  is the null set. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 3 1. Fundamentals ➢ Two regions Ri and Rj are said to be adjacent if their union forms a connected set. ➢ The regions are said to disjoint If the set formed by the union of two regions is not connected. ➢ The fundamental problem in segmentation is to partition an image into regions that satisfy the preceding conditions. ➢ Segmentation algorithms for monochrome images generally are based on one of two basic categories dealing with properties of intensity values: discontinuity and similarity. Edge-based segmentation Region-base segmentation Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 4 1. Fundamentals (a) Image of a constant intensity region. (b) Boundary based on intensity discontinuities. (c) Result of segmentation. (d) Image of a texture region. (e) Result of intensity discontinuity computations (note the large number of small edges). (f) Result of segmentation based on region properties. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 5 1. Fundamentals ❖ Traditional Image Segmentation techniques 6 Chapter 6. Image Segmentation 1. Fundamentals 2. Point, Line, and Edge Detection 3. Thresholding 4. Image Segmentation Using Deep Learning Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 7 2. Point, Line, and Edge Detection ➢ Detecting sharp, local changes in intensity ➢