ece542-12 - ECE542-12 Digital Image Processing Image...

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1 ECE542-12 Digital Image Processing Image Segmentation Dr. Z. Aliyazicioglu Cal Poly Pomona Electrical & Computer Engineering 1 Office 9-143 Outline Discontinuity Detection Point, edge, line Edge Linking and boundary detection Thresholding Region based segmentation Segmentation by morphological watersheds Motion segmentation ECE542 - 12 2 Cal Poly Pomona
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2 What is Image Segmentation? Segmentation: Split or separate an image into regions To facilitate recognition, understanding, and region of interests (ROI) processing III-defined problem The definition of a region is context- dependent 20 40 60 ECE542 - 12 3 Cal Poly Pomona 20 40 60 Definition of A Region Define P as a predicate operating on a region. For a pixel x , ( ) = true if satisfies a specific property. Predicate Examples Gray scale values within a range (threshold) Gradient of gray scale values within a range (edge) Statistical distributions are the same (texture) After applying the predicate, the image becomes a binary ECE542 - 12 4 Cal Poly Pomona image. Using pixel connectivity definitions, a region then can be defined.
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3 Overview of Segmentation Edge based segmentation Finding boundary between adjacent regions Threshold based segmentation Finding regions by grouping pixels with similar gray-values Region based segmentation Finding regions directly using growing or splitting Motion based segmentation ECE542 - 12 5 Cal Poly Pomona Finding regions by comparing successive frames of a video sequence to identify regions that correspond to moving objects Edge-Based Segmentation Finding dis-continuity as boundary of regions Discontinuities in an image Discontinuities in an image: Point Edge Techniques Point detection Edge (pixel) detection Edge formation from edge pixels ECE542 - 12 6 Cal Poly Pomona Edge linking Hough transformation
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4 Point Detection 9 1 i i i z w R z: gray level of the associated pixel T R z i : gray level of the associated pixel Eq.10.1.2 ECE542 - 15 7 Cal Poly Pomona Line Detection ECE542 - 12 8 Cal Poly Pomona Useful for detecting lines with width = 1.
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5 Edge Detection Points and lines are special cases of edges. Edge detection is difficult since it is not clear what Edge detection is difficult since it is not clear what amounts to an edge! ECE542 - 12 9 Cal Poly Pomona Edge Detection ECE542 - 12 10 Cal Poly Pomona
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6 Impact of Noise ECE542 - 12 11 Cal Poly Pomona First column: gray level image Second column: first derivative of the gray level image Third column: second derivative of the gray level image Added Gaussian noise First & Second Derivatives of Edges /
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This note was uploaded on 10/24/2010 for the course ECE 542 taught by Professor Zeki during the Spring '10 term at Cal Poly Pomona.

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ece542-12 - ECE542-12 Digital Image Processing Image...

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