Skip to Main content Skip to Navigation
New interface
Conference papers

How to Overcome Perceptual Aliasing in ASIFT?

Nicolas Noury 1 Frédéric Sur 1 Marie-Odile Berger 1 
1 MAGRIT - Visual Augmentation of Complex Environments
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : SIFT is one of the most popular algorithms to extract points of interest from images. It is a scale+rotation invariant method. As a consequence, if one compares points of interest between two images subject to a large viewpoint change, then only a few, if any, common points will be retrieved. This may lead subsequent algorithms to failure, especially when considering structure and motion or object recognition problems. Reaching at least affine invariance is crucial for reliable point correspondences. Successful approaches have been recently proposed by several authors to strengthen scale+rotation invariance into affine invariance, using viewpoint simulation (e.g. the ASIFT algorithm). However, almost all resulting algorithms fail in presence of repeated patterns, which are common in man-made environments, because of the so-called perceptual aliasing. Focusing on ASIFT, we show how to overcome the perceptual aliasing problem. To the best of our knowledge, the resulting algorithm performs better than any existing generic point matching procedure.
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download
Contributor : Frédéric Sur Connect in order to contact the contributor
Submitted on : Monday, September 6, 2010 - 4:23:03 PM
Last modification on : Thursday, January 20, 2022 - 5:30:24 PM
Long-term archiving on: : Tuesday, October 23, 2012 - 3:36:44 PM


Files produced by the author(s)




Nicolas Noury, Frédéric Sur, Marie-Odile Berger. How to Overcome Perceptual Aliasing in ASIFT?. 6th International Symposium on Visual Computing - ISVC 2010, Nov 2010, Las Vegas, United States. pp.231-242, ⟨10.1007/978-3-642-17289-2_23⟩. ⟨inria-00515375⟩



Record views


Files downloads