segmentation in image processing .pptx

segmentation in image processing

1. Digital Image Processing

2. 10 . I m age S eg m enta t i on - 2 Segmentation attempts to partition the pixels of an image into groups that strongly correlate with the objects in an image Typically the first step in any automated computer vision application Image Segmentation

3. Image Segmentation Segmentation algorithms generally are based on one of two basis properties of intensity values Discontinuity : to partition an image based on abrupt changes in intensity (such as edges) Similarity : to partition an image into regions that are similar according to a set of predefined criteria. 10 . I m age S eg m enta t i on - 3

4. Image Segmentation Image Segmentation 10 . I m age S eg m enta t i on - 4

5. Image Segmentation Image Segmentation 10 . I m age S eg m enta t i on - 5

6. Image Segmentation Detection of discontinuities: There are three basic types of gray-level discontinuities: points , lines , edges the common way is to run a mask through the image 10 . I m age S eg m enta t i on - 6

7. Point Detection : The only differences that are considered of interest are those large enough (as determined by T) to be considered isolated points. |R| >T 10 . I m age S eg m enta t i on - 7

8. Point Detection : 10 . I m age S eg m enta t i on - 8

9. Line Detection Horizontal mask will result with max response when a line passed through the middle row of the mask with a constant background. the similar idea is used with other masks. note: the preferred direction of each mask is weighted with a larger coefficient (i.e.,2) than other possible directions. R 1 R 2 R 3 R 4 10 . I m age S eg m enta t i on - 9

10. Line Detection Apply every masks on the image let R 1 , R 2 , R 3 , R 4 denotes the response of the horizontal, +45 degree, vertical and -45 degree masks, respectively. if, at a certain point in the image |R i | > |R j |, for all j ≠ i, that point is said to be more likely associated with a line in the direction of mask i. 10 . I m age S eg m enta t i on - 10

11. Line Detection Alternatively, if we are interested in detecting all lines in an image in the direction defined by a given mask, we simply run the mask through the image and threshold the absolute value of the result. The points that are left are the strongest responses, which, for lines one pixel thick, correspond closest to the direction defined by the mask. 10 . I m age S eg m enta t i on - 11

12. Line Detection 10 . I m age S eg m enta t i on - 12

13. Edge Detection Approach Segmentation by finding pixels on a region boundary. Edges found by looking at neighboring pixels. Region boundary formed by measuring gray value differences between neighboring pixels

14. Edge Detection an edge is a set of connected pixels that lie on the boundary between two regions. an edge is a “local” concept whereas a region boundary, owing to the way it is defined, is a more global idea. 10 . I m age S eg m enta t i on - 14

15. Edge Detection 10 . I m age S eg m enta t i on - 15

16. Edge Detection 10 . I m age S eg m enta t i on - 16

17. Edge Detection 10 . I m age S eg m enta t i on - 17

18. Edge Detection Detection of discontinuities: Image Derivatives 10 . I m age S eg m enta t i on - 18

19. Edge Detection First column: images and gray- level profiles of a ramp edge corrupted by random Gaussian noise of mean 0 and  = 0.0, 0.1, 1.0 and 10.0, respectively. Second column: first-derivative images and gray-level profiles. Third column : second- derivative images and gray- level profiles. 10 . I m age S eg m enta t i on - 19

20. Edge Detection 1 / 2 2 2 x y for 3  3 ma s k G x  ( z 7  z 8  z 9 )  ( z 1  z 2  z 3 ) G y  ( z 3  z 6  z 9 )  ( z 1  z 4  z 7 )  f   G  G       y     f  Gradient Operator   f  y   G    f   G x     x  10 . I m age S eg m enta t i on - 20

21. Edge Detection Prewitt and Sobel Operators 10 . I m age S eg m enta t i on - 21

22. Edge Detection 10 . I m age S eg m enta t i on - 22

23. Edge Detection 10 . I m age S eg m enta t i on - 23

24. Edge Detection 10 . I m age S eg m enta t i on - 24

25. Edge Detection 10 . I m age S eg m enta t i on - 25

26. Edge Detection The Laplacian 10 . I m age S eg m enta t i on - 26

27. Edge Detection 10 . I m age S eg m enta t i on - 27 Dr. Mostafa GadalHaqq. CS482:Introduction to Digital Image Processing

28. Edge Detection The Laplacian of Gaussian (LoG) 10 . I m age S eg m enta t i on - 28 Dr. Mostafa GadalHaqq. CS482:Introduction to Digital Image Processing

29. Edge Detection The Laplacian of Gaussian (LoG) 10 . I m age S eg m enta t i on - 29 Dr. Mostafa GadalHaqq. CS482:Introduction to Digital Image Processing