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Authors: Grzegorz Kurzejamski ; Jacek Zawistowski and Grzegorz Sarwas

Affiliation: Lingaro Sp. z o.o., Poland

Keyword(s): Computer Vision, Image Analysis, Multiple Object Detection, Object Localization, Pattern Matching.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: This paper presents method for analysis of the vote space created from the local features extraction process in a multi-detection system. The method is opposed to the classic clustering approach and gives a high level of control over the clusters composition for further verification steps. Proposed method comprises of the graphical vote space presentation, the proposition generation, the two-pass iterative vote aggregation and the cascade filters for verification of the propositions. Cascade filters contain all of the minor algorithms needed for effective object detection verification. The new approach does not have the drawbacks of the classic clustering approaches and gives a substantial control over process of detection. Method exhibits an exceptionally high detection rate with conjunction with a low false detection chance in comparison with alternative methods.

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Paper citation in several formats:
Kurzejamski, G.; Zawistowski, J. and Sarwas, G. (2015). Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP; ISBN 978-989-758-090-1; ISSN 2184-4321, SciTePress, pages 252-259. DOI: 10.5220/0005267002520259

@conference{visapp15,
author={Grzegorz Kurzejamski. and Jacek Zawistowski. and Grzegorz Sarwas.},
title={Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP},
year={2015},
pages={252-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005267002520259},
isbn={978-989-758-090-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP
TI - Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features
SN - 978-989-758-090-1
IS - 2184-4321
AU - Kurzejamski, G.
AU - Zawistowski, J.
AU - Sarwas, G.
PY - 2015
SP - 252
EP - 259
DO - 10.5220/0005267002520259
PB - SciTePress

- Science and Technology Publications, Lda.
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