Abstract
A new model to compute similarity is presented. The representation of a 3D object is reviewed; sequence of vertices and index of vertices are the basic information about the shape of any 3D object. A linear function called Labeling is introduced to create a new sequence or time series from a 3D object. A method to create randomly 3D objects is also described. Experimental results show viability to compute similarity among 3D objects using the extracted sequences and the Dynamic Time Warping algorithm.
Keywords
- Dynamic Time Warping
- Computing Similarity
- Random Modification
- Dynamic Time Warping Distance
- Warping Path
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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Angeles-Yreta, A., Figueroa-Nazuno, J. (2005). Computing Similarity Among 3D Objects Using Dynamic Time Warping. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_34
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DOI: https://doi.org/10.1007/11578079_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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