Chapter 10 Image Segmentation. Preview Segmentation subdivides an image into its constituent regions...
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Transcript of Chapter 10 Image Segmentation. Preview Segmentation subdivides an image into its constituent regions...
PreviewSegmentation subdivides an image into its constituent regions or objects.Level of division depends on the problem being solved.Image segmentation algorithms generally are based on one of two basic properties of intensity values: discontinuity (e.g. edges) and similarity (e.g., thresholding, region growing, region splitting and merging)
Chapter OutlineDetection of discontinuitiesEdge linking and boundary detectionThresholdingRegion-based segmentationMorphological watershedsMotion in segmentation
Edge DetectionAn ideal edge has the properties of the model shown to the right: A set of connected pixels, each of which is located at an orthogonal step transition ingray level.Edge: local conceptRegion Boundary: global idea
Ramp Digital EdgeIn practice, optics, sampling and other image acquisition imperfections yield edges that area blurred. Slope of the ramp determined by the degree of blurring.
Edge PointWe define a point in an image as being an edge point if its 2-D 1st order derivative is greater than a specified threshold.A set of such points that are connected according to a predefined criterion of connectedness is by definition an edge.
The LaplacianDefinition:
Generally not used in its original form due to sensitivity to noise.Role of Laplacian in segmentation:
Zero-crossingsTell whether a pixel is on the dark or light side of an edge.
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Global Processing: Hough Transform
Representation of lines in parametric space: Cartesian coordinate
ThresholdingFoundation: background point vs. object point The role of illumination: f(x,y)=i(x,y)*r(x,y)Basic global thresholdingAdaptive thresholdingOptimal global and adaptive thresholdingUse of boundary characteristics for histogram improvement and local thresholdingThresholds based on several variables
Optimal Global and Adaptive Thresholding
Refer to Chapter 2 of the “Pattern Classification” textbook by Duda, Hart and Stork.
Region-Based Segmentation
Let R represent the entire image region. We may view segmentation as a process that partitions R into n sub-regions R1, R2, …, Rn such that:
(a)(b) Ri is a connected region(c)(d) P(Ri)= TRUE for i=1,2,…n(e) P(Ri U Rj)= FALSE for i != j
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