Segmentation and Edge Detection

1. Segmentation and Edge Detection • Segmentation • Splitting an image up into segments (area or regions) • Each segment holds some property distinct from its neighbor • Basic requirement for identification and classification of objects in a scene • Answering questions like: How many object are there? Where is the background? How large is the object? • Segmentation can be approached from two points of view 1. by identifying edges (or lines) that run through an image ( edge operation) 2. by identifying regions (or areas) within an image (region operation) 1 and 2 are correlate: completion of an edge detection is equivalent to breaking one region into two 240-373 Image Processing, Lecture #9

2. Region Operations • Required to cover a substantial area of the scene rather than a small group of pixels Technique 1: Crude region detection USE: To reconsider an image as a set of regions. OPERATION: The regions are simply identified as continuous pixels of the same gray level. The boundaries of the regions are at the cracks between the pixels rather than at pixel positions. • This technique may give too many regions to be useful • bunching (quantizing) technique can be used to reduce number of regions 240-373 Image Processing, Lecture #9

3. Region Merging Technique 2: Region merging USE: To reduce the number of regions, to combine fragment regions, to determine which regions are really part of the same area. OPERATION: • Let s be a crack differece, i.e. the absolute difference in gray levels between two adjacent (above, below, left, or right) pixels • Then given a threshold value, T, we can identify for each crack i.e. w=1 if the crack is below the threshold (the regions are likely to be the same), or 0 if it is above the threshold. • Measure the full length of the boundary of each of the regions that meets at the crack. These will be b1 and b2. • Sum the w’s that are along the length of the crack between the region and calculate • If this is greather than a further threshold, deduce that the two regions should be joined 240-373 Image Processing, Lecture #9

4. Region merging example If we make T=3, then all the cracks are significant Sum of w’s Region Boundary With A With B With C A 17 - 9 0 B 10 9 - 4 C 4 0 4 - 240-373 Image Processing, Lecture #9

5. Giving sum/min(b1,b2) for A, B, and C as follows: Region A B C A - 0.9 0 B 0.9 - 1.0 C 0 1.0 - • If we take the threshold for combining as 0.95, then B will be combined with C. • If the combining threshold is set lower, then A will also be combined giving the whole image as just one region. • The algorithm combines the small region (4) with the larger region (2), rather than the two larger regions (1-2). • Size of the region can be used instead of the length of the region boundary. 240-373 Image Processing, Lecture #9

6. Region Splitting Technique 3: Region splitting USE: To subdivide part of an image into regions of similar type OPERATION: • Identify significant peaks in the gray-level histogram • Look in the valleys between the peaks for possible threshold values. • Find splits between the best peaks first LIMITATIONS: • This technique relies on the overall histogram (Multiple chessboards example) • Example 0 ********************** 1 **** the valley is at 1 2 ********* 3 ***************** 4 ******** 240-373 Image Processing, Lecture #9

7. Which side should the 1’s go? • Consider both options (0), (1,2,3,4) (0,1), (2,3,4) OOOOOO . O . . OOOOOO . O . . OOO . OOO . . . OOOOOOO . . . OO . O . . . . . . OO . O . . . . . . OO . . . . . . . . OOO . . . . . . . O . O . . . . . . . OOO . . . . . . . OO . . . . . . . . OOO . . . . . . . The second option less stragglers than the first but is is difficult to see this without trying it. COMMENT: • The histogram could just be of a region within the image 240-373 Image Processing, Lecture #9