IEG4160_Part8 - IEG 4160: Image and Video Processing....

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Image Segmentation IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu Introduction Detection of discontinuities Edge linking and boundary detection Thresholding Region-based segmentation
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Page 2 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Introduction Objective of image segmentation ± To subdivide an image into its constituent regions or objects An important step for numerous applications related to automation Algorithms based on ± Discontinuity : partitioning an image based on abrupt changes in intensity (e.g. edges) ± Similarity : partitioning an image into regions that are similar according to a set of predefined criteria (e.g. thresholding, region growing)
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Page 3 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Introduction One of the most difficult tasks in image processing/analysis in general cases ± Segmentation accuracy determines the eventual success or failure at later steps ± If possible, control lighting and environment to make segmentation more accurate (e.g. in industrial inspection) ± When lighting and environment are uncontrollable, an approach is to select sensors that can enhance the objects of interest while diminishing irrelevant image details (e.g. infrared imaging to detect objects with strong heat)
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Page 4 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Detection of discontinuities Detection of points, lines and edges by running a mask through images 11 2 2 1 ... mn mn mn i i i R wz w z = =+ + + = : gray level : mask coefficient i i z w 1 z 2 z 3 z 4 z 5 z 6 z 7 z 8 z 9 z
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Page 5 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Detection of discontinuities Point Detection of isolated points ± An isolated point ² significant gray level different from its background ² located in a (nearly) homogeneous area ± A point has been detected at the location on which the mask is centered if || R T 11 2 2 1 ... mn mn mn i i i R wz w z = =+ + + = ( T : a threshold ) 10 20 20 -1 -1 2 -10 A point is detected by T = 15 or so
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Page 6 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Detection of discontinuities An example of point detection A single black pixel T = 90% of the highest |value| in Fig. c .
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Page 7 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Detection of discontinuities Line detection ± Four masks used to detect lines at four directions Responding more strongly to lines oriented at 0, 45, 90 or -45 degree 0 0 0 10 10 10 0 0 0 Part of an image Responses of the four masks: 60, 0, 0 and 0, respectively 0 0 0 10 10 10 10 10 10 Part of an image Responses of the four masks: 30, 0, 0 and 0, respectively
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Page 8 IEG 4160: Image and Video Processing. Lecturer: Jianzhuang Liu 8. Image Segmentation Detection of discontinuities How to use the masks ± Detection of lines at the four directions ² Run the four masks individually through an image ² At a certain point, choose the largest absolute response
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IEG4160_Part8 - IEG 4160: Image and Video Processing....

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