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dc.contributor.authorTaylor, Russell
dc.contributor.authorKazhdan, Michael
dc.contributor.authorLucas, Blake
dc.date.accessioned2012-02-28T01:02:21Z
dc.date.available2012-02-28T01:02:21Z
dc.date.issued2012-02-28T01:02:21Z
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/35726
dc.description.abstractAn important task for computer vision systems is to segment adjacent structures in images without producing gaps or overlaps. Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel accuracy. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel accuracy. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the segmentation algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label image and unsigned distance field. The time complexity of the algorithm is shown to be O((M^d)/P) for M^d pixels and P processing units in dimension d={2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems.en
dc.language.isoen_USen
dc.relation.ispartofseriesJohns Hopkins University Department of Computer Science;Technical Report 12-01
dc.titleMulti-Object Geodesic Active Contours (MOGAC): A Parallel Sparse-Field Algorithm for Image Segmentationen
dc.typeArticleen


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