Normalization of a 3D-Shape Similarity Measure with Voxel Representation
In this paper we study some properties of 3D objects such as compactness, the work done in object transformations and the number of voxels to be moved in order to normalize a similarity measure of an appropriate set of 3D objects. Voxel representation and scale normalization allow us to find the total distance of a set of voxels from one object to another. For these purposes, the comparison of objects is achieved by superimposing their centers of mass, using principal axes for their orientation, and Hungarian algorithm for optimal matching in bipartite graphs. All these aspects are determinant in obtaining the minimum work that needs to be done in the corresponding transformations. We present experimental results by including irregular objects taken from the human body.
| Main Authors: | , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Instituto Politécnico Nacional, Centro de Investigación en Computación
2007
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| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462007000200005 |
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| Summary: | In this paper we study some properties of 3D objects such as compactness, the work done in object transformations and the number of voxels to be moved in order to normalize a similarity measure of an appropriate set of 3D objects. Voxel representation and scale normalization allow us to find the total distance of a set of voxels from one object to another. For these purposes, the comparison of objects is achieved by superimposing their centers of mass, using principal axes for their orientation, and Hungarian algorithm for optimal matching in bipartite graphs. All these aspects are determinant in obtaining the minimum work that needs to be done in the corresponding transformations. We present experimental results by including irregular objects taken from the human body. |
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