Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binaryimages, labeling is required. This paper presents two efficient label-equivalence-based connected-component labelin...
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Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binaryimages, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-d binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-d binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-d labeling algorithms.
In this paper we have presented an algorithm for the extraction of the wire frame structure of a given 3-d object. The 3-d input object is assumed to be in a binary array. This object is first thinned and then segment...
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In this paper we have presented an algorithm for the extraction of the wire frame structure of a given 3-d object. The 3-d input object is assumed to be in a binary array. This object is first thinned and then segmented into planar segments. After segmentation, every segment of the object is polygonized using one of the two techniques: namely, Graham Polygonization and Boundary Traversal. Adjacent polygonized segments are then connected through vertex-merging. Finally the obtained graph is post-processed to remove some unwanted offshoots that result in the extraction. We also present some experimental results of wire frame extraction.
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