Lecture-10-h - Lecture-10 Region Merging & Region...

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1 Lecture-10 Region Merging Region Properties Segmentation
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2 Segmentation Partition f ( x,y ) into sub-images: R 1 , R 2 , ….,R n such that the following constraints are satisfied: – Each sub-mage satisfies a predicate or set of predicates • All pixels in any sub-image musts have the same gray levels. • All pixels in any sub-image must not differ more than some threshold • All pixels in any sub-image may not differ more than some threshold from the mean of the gray of the region • The standard deviation of gray levels in any sub-image must be small. j i R R j i = , f I ) , ( 1 y x f R i n i = = U Initial Segmentation
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3 Applications of Segmentation • Object recognition • MPEG-4 video compression Object Recognition Using Region Properties • Training – For all training samples of each model object • Segment the image • Compute region properties (features) – Compute mean feature vector for each model object • Recognition – Given an image of unknown object, • segment the image • compute its feature vector • match the vector to all possible models to determine its identity.
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4 Object-Based Compression (MPEG-4) • Advantages of OBC – large increase in compression ratio – allows manipulation of compressed video (inserting, deleting and modifying objects) • How does it work? – Find objects (
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Lecture-10-h - Lecture-10 Region Merging & Region...

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