Yu, H. and Bennamoun, M., 2006. Simplified Representation of Large or Medium Range Dataset. In: VISAPP 2006: Proceedings of International Conference on Computer Vision Theory and Applications 2006. INSTICC - Institute for Systems and Technologies of Information, Control and Communication.
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Official URL: http://visapp.visigrapp.org/Abstracts/2006/VISAPP_...
In this paper, we consider two approaches of simplifying medium- and large-sized range datasets to a compact data point set, based on the Radial Basis Functions application. The first algorithm uses the Pseudo-Inverse Approach for the case of given basis functions, and the second one uses an SVD-Based Approach for the case of unknown basis functions. The novelty of this paper consists in a novel partition-based SVD algorithm for a symmetric square matrix, which can effectively reduce the dimension of a matrix in a given partition case. On the theoretical aspect, it is proven that the reduced matrix is an optimal approximation in least square sense. Furthermore, this partition-based SVD algorithm is combined with a standard clustering algorithm to form our SVD-Based Approach, which can then seek an appropriate partition automatically for dataset simplification. Experimental results indicate that the presented Pseudo-Inverse Approach requires a uniform sampled control point set, and can obtain an optimal least square solution in the given control point set case. While in the unknown control point case, the presented SVD-Based Approach can seek an appropriate control point set automatically, and the resulting surface preserves more of the essential structures and is prone to less distortions.
|Item Type:||Book Section|
|Additional Information:||25-28 February 2006, Setúbal, Portugal.|
|Uncontrolled Keywords:||Range dataset, Simplification, Radial Basis Functions, SVD decomposition|
|Subjects:||Generalities > Computer Science and Informatics|
|Group:||Media School > National Centre for Computer Animation|
|Deposited By:||Mr Hongchuan Yu|
|Deposited On:||23 May 2010 20:52|
|Last Modified:||07 Mar 2013 15:29|
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