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Authors: Steven Puttemans and Toon Goedemé

Affiliation: KU Leuven, Belgium

ISBN: 978-989-8565-47-1

Keyword(s): Object Categorization, Industrial Applications, Input Constraints, Object Localization.

Related Ontology Subjects/Areas/Topics: Applications ; Pattern Recognition ; Robotics ; Software Engineering

Abstract: State-of-the-art object categorization algorithms are designed to be heavily robust against scene variations like illumination changes, occlusions, scale changes, orientation and location differences, background clutter and object intra-class variability. However, in industrial machine vision applications where objects with variable appearance have to be detected, many of these variations are in fact constant and can be seen as constraints on the scene, which in turn can reduce the enormous search space for object instances. In this position paper we explore the possibility to fixate certain of these variations according to the application specific scene constraints and investigate the influence of these adaptations on three main aspects of object categorization algorithms: the amount of training data needed, the speed of the detection and the amount of false detections. Moreover, we propose steps to simplify the training process under such scene constraints.

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Paper citation in several formats:
Puttemans, S. and Goedemé, T. (2013). How to Exploit Scene Constraints to Improve Object Categorization Algorithms for Industrial Applications?.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 827-830. DOI: 10.5220/0004342108270830

@conference{visapp13,
author={Steven Puttemans. and Toon Goedemé.},
title={How to Exploit Scene Constraints to Improve Object Categorization Algorithms for Industrial Applications?},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={827-830},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004342108270830},
isbn={978-989-8565-47-1},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - How to Exploit Scene Constraints to Improve Object Categorization Algorithms for Industrial Applications?
SN - 978-989-8565-47-1
AU - Puttemans, S.
AU - Goedemé, T.
PY - 2013
SP - 827
EP - 830
DO - 10.5220/0004342108270830

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