AN ALTERNATIVE TO SCALE-SPACE REPRESENTATION FOR EXTRACTING LOCAL FEATURES IN IMAGE RECOGNITION

Hans Jørgen Andersen, Giang Phuong Nguyen

2012

Abstract

In image recognition, the common approach for extracting local features using a scale-space representation has usually three main steps; first interest points are extracted at different scales, next from a patch around each interest point the rotation is calculated with corresponding orientation and compensation, and finally a descriptor is computed for the derived patch (i.e. feature of the patch). To avoid the memory and computational intensive process of constructing the scale-space, we use a method where no scale-space is required This is done by dividing the given image into a number of triangles with sizes dependent on the content of the image, at the location of each triangle. In this paper, we will demonstrate that by rotation of the interest regions at the triangles it is possible in grey scale images to achieve a recognition precision comparable with that of MOPS. The test of the proposed method is performed on two data sets of buildings.

References

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Paper Citation


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - AN ALTERNATIVE TO SCALE-SPACE REPRESENTATION FOR EXTRACTING LOCAL FEATURES IN IMAGE RECOGNITION
SN - 978-989-8565-03-7
AU - Jørgen Andersen H.
AU - Phuong Nguyen G.
PY - 2012
SP - 341
EP - 345
DO - 10.5220/0003836203410345


in Harvard Style

Jørgen Andersen H. and Phuong Nguyen G. (2012). AN ALTERNATIVE TO SCALE-SPACE REPRESENTATION FOR EXTRACTING LOCAL FEATURES IN IMAGE RECOGNITION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 341-345. DOI: 10.5220/0003836203410345


in Bibtex Style

@conference{visapp12,
author={Hans Jørgen Andersen and Giang Phuong Nguyen},
title={AN ALTERNATIVE TO SCALE-SPACE REPRESENTATION FOR EXTRACTING LOCAL FEATURES IN IMAGE RECOGNITION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={341-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003836203410345},
isbn={978-989-8565-03-7},
}