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The Eigen-transform and applications
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2006 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a novel texture descriptor, the Eigen-transform. The transform provides a measure of roughness by considering the eigenvalues of a matrix which is formed very simply by inserting the greyvalues of a square patch around a pixel directly into a matrix of the same size. The eigenvalue of largest magnitude turns out to give a smoothed version of the original image, but the eigenvalues of smaller magnitude encode high frequency information characteristic of natural textures. A major advantage of the Eigen-transform is that it does not fire on straight, or locally straight, brightness edges, instead it reacts almost entirely to the texture itself. This is in contrast to many other descriptors such as Gabor filters or the standard deviation of greyvalues of the patch. These properties make it remarkably well suited to practical applications. Our experiments focus on two main areas. The first is in bottom-up visual attention where textured objects pop out from the background using the Eigen-transform. The second is unsupervised texture segmentation with particular emphasis on real-world, cluttered indoor environments. We compare results with other state-of-the-art methods and find that the Eigen-transform is highly competitive, despite its simplicity and low dimensionality.

Place, publisher, year, edition, pages
2006. 70-79 p.
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743
Keyword [en]
Computer vision, Image processing, Mathematical transformations, Matrix algebra, Textures, Eigenvalues, State-of-the-art methods, Texture descriptor, Eigenvalues and eigenfunctions
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-156333DOI: 10.1007/11612032_8Scopus ID: 2-s2.0-33744947992ISBN: 3540312196 (print)ISBN: 9783540312192 (print)OAI: oai:DiVA.org:kth-156333DiVA: diva2:766734
Conference
7th Asian Conference on Computer Vision, ACCV 2006, 13-16 January 2006, Hyderabad, India
Note

QC 20141128

Available from: 2014-11-28 Created: 2014-11-26 Last updated: 2017-03-24Bibliographically approved

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CiteExportLink to record
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