This paper presents an efficient feature point descriptor for non-rigid shape analysis. The descriptor is developed based on the properties of the heat diffusion process on a shape. We use, for the first time, the Heat Kernel Signature of a particular time scale to define the scalar field on a manifold. Then, motivated by the successful use of a local reference frame for rigid shape analysis, we construct a repetitive local polar coordinate system, which is invariant under isometric deformations. Finally, a binary descriptor is derived by comparing the intensities of the neighboring points for each feature point. We show that the descriptor is highly discriminative and can be computed simply using ‘intensity comparisons’ on a shape. Furthermore, its similarity can be evaluated using the Hamming distance, which is very efficient to compute compared with the commonly used L2L2 norm. Our experiments demonstrate a superior performance compared to existing techniques on the standard benchmark TOSCA.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||7th Pacific-Rim Symposium on Image and Video Technology|
|Abbreviated title||PSIVT 2015|
|Period||23/11/15 → 27/11/15|