ch10b - Ch10 Segmentation Overview Thresholding Region...

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Ch10 – Segmentation • Overview • Thresholding • Region Growing • Split and Merge • Watershed Segmentation • Conclusion
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Overview • The purpose of segmentation is to divide an image into “visually sensible” regions corresponding to objects of interest • Segmentation is the “dual” of edge detection where we focus on finding object boundaries • In most cases, the pixels inside a region are homogeneous (same brightness or color)
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Overview • Different segmentation algorithms have been devised to group similar pixels together to form regions for each object of interest – Global – thresholding – Bottom up – region growing – Top down – split and merge – Geometric – watershed • Once regions are found, we often need to calculate their properties (size, position, shape)
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Thresholding • The basic idea of image thresholding is to divide the intensity range in an image into two categories based on a threshold value T – Background = {0. .T-1} – Object = {T. .255} • The key is finding the threshold value T that produces the most sensible results
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Thresholding • Many interactive image processing systems allow users to select thresholds manually • Some systems use a priori domain knowledge to select appropriate thresholds (eg. fax) • Most automatic threshold selection methods are based on the intensity distribution of the input image and optimize some quality metric
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Thresholding • The mean midpoint threshold algorithm selects a threshold value T to be the midpoint between the mean object intensity and the mean background intensity – Guess initial T – Calculate M 1 = mean < T – Calculate M 2 = mean > T – Update T=(M 1 +M 2 )/2 – Iterate until convergence
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Thresholding original image threshold image histogram
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This note was uploaded on 12/01/2011 for the course CSCE 5013 taught by Professor Staff during the Fall '08 term at Arkansas.

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ch10b - Ch10 Segmentation Overview Thresholding Region...

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