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Authors: Mingyuan Jiu ; Christian Wolf and Atilla Baskurt

Affiliation: Université de Lyon, CNRS, INSA-Lyon, LIRIS and UMR5205, France

ISBN: 978-989-8565-47-1

ISSN: 2184-4321

Keyword(s): Object Detection, Pose Estimation, Spatial Layout, Unary Terms, Randomized Decision Forest, Kinect.

Abstract: Object recognition or human pose estimation methods often resort to a decomposition into a collection of parts. This local representation has significant advantages, especially in case of occlusions and when the “object” is non-rigid. Detection and recognition requires modeling the appearance of the different object parts as well as their spatial layout. The latter can be complex and requires the minimization of complex energy functions, which is prohibitive in most real world applications and therefore often omitted. However, ignoring the spatial layout puts all the burden on the classifier, whose only available information is local appearance. We propose a new method to integrate the spatial layout into the parts classification without costly pairwise terms. We present an application to body parts classification for human pose estimation. As a second contribution, we introduce edge features from gray images as a complement to the well known depth features used for body parts classif ication from Kinect data. (More)

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Paper citation in several formats:
Jiu, M.; Wolf, C. and Baskurt, A. (2013). Integrating Spatial Layout of Object Parts into Classification without Pairwise Terms - Application to Fast Body Parts Estimation from Depth Images.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 626-631. DOI: 10.5220/0004278206260631

@conference{visapp13,
author={Mingyuan Jiu. and Christian Wolf. and Atilla Baskurt.},
title={Integrating Spatial Layout of Object Parts into Classification without Pairwise Terms - Application to Fast Body Parts Estimation from Depth Images},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={626-631},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004278206260631},
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 - Integrating Spatial Layout of Object Parts into Classification without Pairwise Terms - Application to Fast Body Parts Estimation from Depth Images
SN - 978-989-8565-47-1
AU - Jiu, M.
AU - Wolf, C.
AU - Baskurt, A.
PY - 2013
SP - 626
EP - 631
DO - 10.5220/0004278206260631

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