Lecture_16.pdf - CSE 4227 Digital Image Processing Lecture...

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07/07/2017 1 CSE 4227: Digital Image Processing Lecture 16 Image Segmentation – Overall Review Dr S Rahman Professor, CSE, AUST CSE | AUST 2 of 28 Segmentation Dividing the image into different regions. Separating objects from background and giving them individual ID numbers (labels). 2
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07/07/2017 2 3 of 28 Segmentation…cont…. Accurate segmentation of objects of interest in an image greatly facilitates further analysis of these objects. For example, it allows us to: Count the number of objects of a certain type. Measure geometric properties (e.g., area, perimeter) of objects in the image. Study properties of an individual object (intensity, texture, etc.) 3 4 of 28 4
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07/07/2017 3 5 of 28 5 6 Targeted Segmentation A segmentation can also be defined as a mapping from the set of pixels to some application dependent target set, e.g. • {Object, Background} • {Humans, Other objects} • {1,2,3,4,...} • {Healthy tissue, Tumors} To perform accurate segmentation, we (or our algorithms) need to somehow know how to differentiate between different elements of the target set.
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07/07/2017 4 7 of 28 Segmentation….cont….. Segmentation algorithms are often based on one of the following two basic properties of intensity values: Similarity Partitioning an image into regions that are similar according to a set of predefines criteria.
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