Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/55479
Type: Conference paper
Title: 3D object pose inference via kernel principal component analysis with image euclidian distance (IMED).
Author: Tangkuampien, T.
Suter, D.
Citation: Proceedings of the 17th British Machine Vision Conference, Edinburgh, U.K., 2006
Publisher: British Machine Vision Association
Publisher Place: Online
Issue Date: 2006
ISBN: 1904410146
Conference Name: British Machine Vision Conference (17th : 2006 : Edinburgh)
Statement of
Responsibility: 
T. Tangkuampien and D. Suter
Abstract: Kernel Principal Component Analysis (KPCA) is a powerful non-linear unsupervised learning technique for high dimensional pattern analysis. KPCA on images, however, usually considers each image pixel as an independent dimension and does not take into account the spatial relationship of nearby pixels. In this paper, we show how the Image Euclidian Distance (IMED), which takes into account local pixel intensities, can efficiently be embedded into KPCA via the Kronecker product and Eigenvector projections, whilst still retaining desirable properties of Euclidian distance (such as kernel positive definitiveness and effective image de-noising). We demonstrate that KPCA with embedded IMED is a more intuitive and accurate technique than standard KPCA through a 3D object pose estimation application.
RMID: 0020094147
Description (link): http://www.macs.hw.ac.uk/bmvc2006/proceedings.html
Appears in Collections:Computer Science publications

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