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APPROXIMATION OF PATTERN TRANSFORMATION MANIFOLDS WITH PARAMETRIC DICTIONARIES

Image Feature Extraction and Analysis

Full Paper at IEEE Xplore

Presented by: Elif Vural, Author(s): Elif Vural, Pascal Frossard, Ecole Polytechnique Fédérale de Lausanne, Switzerland

The construction of low-dimensional models explaining high-dimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern transformation manifold models generated by in-plane geometric transformations of 2D visual patterns. We propose a method for computing a manifold by building a representative pattern such that its transformation manifold accurately fits a set of given observations. We present a solution for the progressive construction of the representative pattern with the aid of a parametric dictionary, which in turn provides an analytical representation of the data and the manifold. Experimental results show that the patterns learned with the proposed algorithm can efficiently capture the main characteristics of the input data with high approximation accuracy, where the invariance to the geometric transformations of the data is accomplished due to the transformation manifold model.


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  Lecture Information

Recorded: 2011-05-27 15:25 - 15:45, Club A
Added: 15. 6. 2011 01:22
Number of views: 27
Video resolution: 1024x576 px, 512x288 px
Video length: 0:21:12
Audio track: MP3 [7.17 MB], 0:21:12