9 Segmentation, morphology and blob analysis
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9.1 Thresholding methods
9.1.1 Global thresholds and manual selection
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9.1.2 Otsu’s method
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9.1.3 Adaptive and locally-adaptive thresholding
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9.1.4 Multi-level and hysteresis thresholding
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9.2 Region-based segmentation
9.2.1 Region growing and merging
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9.2.2 Watershed segmentation
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9.2.3 Superpixels: SLIC and successors
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9.2.4 Graph-cut segmentation
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9.3 Edge-based segmentation
9.3.1 The Canny detector
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9.3.2 Edge linking and boundary tracing
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9.3.3 Active contours (snakes) and level sets
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9.4 Mathematical morphology
9.4.1 Structuring elements
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9.4.2 Erosion and dilation
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9.4.3 Opening and closing
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9.4.4 Hit-or-miss transform and skeletonisation
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9.4.5 Distance transforms and reconstruction
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9.4.6 Greyscale morphology
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9.5 Connected-components and blob analysis
9.5.1 Connectivity and labelling algorithms
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9.5.2 Region properties: area, perimeter, centroid
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9.5.3 Geometric moments and Hu invariants
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9.5.4 Shape descriptors: circularity, eccentricity, compactness
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9.5.5 Filtering blobs by property
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9.6 Choosing a segmentation approach
9.6.1 Thresholding versus region versus edge versus learned
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9.6.2 Reliability under illumination and contrast variation
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9.6.3 Combining segmentation with morphological clean-up
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