| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2001: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2000: ¥2,800,000 (Direct Cost: ¥2,800,000)
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| Research Abstract |
During this last fiscal year, we foctised our effort on tlie study of digital watermarking and shape similarity search of 3D models.
1. Digital Watermarking : In the last fiscal year, we developed a new digital watermarking algorithm for 3D polygonal meshes that employs the mesh spectral analysis technique (We published the results in the IPSJ Journal and at the Graphics Interface 2001). Watermarks embedded by using this method are resilient -against additive random noise, mesh smoothing, and other interferences. At the same time, the method has a high information payload (i.e., information bits embedded per number of vertices). However, the method has a pair of weakness, that are, the watermarks are destroyed if connectivity of the mesh is disturbed (e.g., by mesh simplification), and that the computational cost of mesh spectral analysis is quite high, taking hours to watermark a mesh having a few thousands of vertices. In this fiscal year, we refined the algorithm quite a bit to overc
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ome these two weaknesses. Namely, we improved in three areas ; (1) by employing an efficient eigen-decomposition algorithm, and (2) by per-patch alignment of meshes after geometrical transformations, and (3) by recovering the original mesh connectivity through remeshing. We implemented and evaluated the improved algorithm. (The results of the new algorithm will be presented at EUROGRAPHICS '2002 in September 2002.)Separately, we have improved our digital watermarking algorithm for vector digital maps in order to give it a resiliency against cropping.
2. Compression We have not worked on compression in this fiscal year.
3. Shape Similarity Search : We studied various shape features and methods to compare the features. Shape similarity search algorithms have two common difficulties : (1) Alignment of models is necessary for proper comparison of similarity, and (2) a similarity computed mechanically often misses the result based on human perception. In this fiscal year we developed a new shape similarity search method by combining a shape feature due to Osada, et al. and a learning classifier called Support Vector Machine. Our preliminary results show that the method reflects human perception/cognition and is quite robust against irregularities in the model such as topological error and geometrical degeneracies. Less
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