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SPEEDED UP IMAGE MATCHING USING SPLIT AND EXTENDED SIFT FEATURES

Topics: Active and Robot Vision; Image Registration; Localization and Recognition of 3D Object Features; Matching Correspondence and Flow; Object, Event and Scene Recognition, Retrieval and Indexing; Pattern Recognition in Image Understanding; Real-Time Vision; Recognition and Indexing; Stereo Vision and Structure from Motion

Authors: Faraj Alhwarin ; Danijela Ristić –Durrant and Axel Gräser

Affiliation: University of Bremen, Germany

ISBN: 978-989-674-028-3

Keyword(s): Speeded Up Features Matching, Split SIFT, Extended SIFT.

Related Ontology Subjects/Areas/Topics: Active and Robot Vision ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Image Registration ; Matching Correspondence and Flow ; Methodologies and Methods ; Motion, Tracking and Stereo Vision ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Real-Time Vision ; Sensor Networks ; Soft Computing ; Stereo Vision and Structure from Motion

Abstract: Matching feature points between images is one of the most fundamental issues in computer vision tasks. As the number of feature points increases, the feature matching rapidly becomes a bottleneck. In this paper, a novel method is presented to accelerate features matching by two modifications of the popular SIFT algorithm. The first modification is based on splitting the SIFT features into two types, Maxima- and Minima-SIFT features, and making comparisons only between the features of the same type, which reduces the matching time to 50% with respect to the original SIFT. In the second modification, the SIFT feature is extended by a new attribute which is an angle between two independent orientations. Based on this angle, SIFT features are divided into subsets and only the features with the difference of their angles less than a pre-set threshold value are compared. The performance of the proposed methods was tested on two groups of images, real-world stereo images and standard dataset images. The presented experimental results show that the feature matching step can be accelerated 18 times with respect to exhaustive search without losing a noticeable portion of correct matches. (More)

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Paper citation in several formats:
Alhwarin F.; Ristić –Durrant D.; Gräser A. and (2010). SPEEDED UP IMAGE MATCHING USING SPLIT AND EXTENDED SIFT FEATURES.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 287-295. DOI: 10.5220/0002820102870295

@conference{visapp10,
author={Faraj Alhwarin and Danijela {Ristić –Durrant} and Axel Gräser},
title={SPEEDED UP IMAGE MATCHING USING SPLIT AND EXTENDED SIFT FEATURES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={287-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002820102870295},
isbn={978-989-674-028-3},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - SPEEDED UP IMAGE MATCHING USING SPLIT AND EXTENDED SIFT FEATURES
SN - 978-989-674-028-3
AU - Alhwarin, F.
AU - Ristić –Durrant, D.
AU - Gräser, A.
PY - 2010
SP - 287
EP - 295
DO - 10.5220/0002820102870295

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