segmentation in image processing .pptx
- 2. 10. Image Segmentation - 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. Image Segmentation - 3
- 4. Image Segmentation Image Segmentation 10. Image Segmentation - 4
- 5. Image Segmentation Image Segmentation 10. Image Segmentation - 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. Image Segmentation - 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. Image Segmentation - 7
- 8. Point Detection: 10. Image Segmentation - 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. R1 R2 R3 R4 10. Image Segmentation - 9
- 10. Line Detection • Apply every masks on the image • let R1, R2, R3, R4 denotes the response of the horizontal, +45 degree, vertical and - 45 degree masks, respectively. • if, at a certain point in the image |Ri| > |Rj|, for all j≠i, • that point is said to be more likely associated with a line in the direction of mask i. 10. Image Segmentation - 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. Image Segmentation - 11
- 12. Line Detection 10. Image Segmentation - 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. Image Segmentation - 14
- 15. Edge Detection 10. Image Segmentation - 15
- 16. Edge Detection 10. Image Segmentation - 16
- 17. Edge Detection 10. Image Segmentation - 17
- 18. Edge Detection Detection of discontinuities: Image Derivatives 10. Image Segmentation - 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. Image Segmentation - 19
- 20. Edge Detection 1/ 2 2 2 x y for 3 3 mask Gx (z7 z8 z9 ) (z1 z2 z3 ) Gy (z3 z6 z9 ) (z1 z4 z7 ) f G G y f Gradient Operator f y G f Gx x 10. Image Segmentation - 20
- 21. Edge Detection Prewitt and Sobel Operators 10. Image Segmentation - 21
- 22. Edge Detection 10. Image Segmentation - 22
- 23. Edge Detection 10. Image Segmentation - 23
- 24. Edge Detection 10. Image Segmentation - 24
- 25. Edge Detection 10. Image Segmentation - 25
- 26. Edge Detection The Laplacian 10. Image Segmentation - 26
- 27. Edge Detection 10. Image Segmentation - 27 Dr. Mostafa GadalHaqq. CS482:Introduction to Digital Image Processing
- 28. Edge Detection The Laplacian of Gaussian (LoG) 10. Image Segmentation - 28 Dr. Mostafa GadalHaqq. CS482:Introduction to Digital Image Processing
- 29. Edge Detection The Laplacian of Gaussian (LoG) 10. Image Segmentation - 29 Dr. Mostafa GadalHaqq. CS482:Introduction to Digital Image Processing