Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108880
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Type: Conference paper
Title: Probabilistic object models for pose estimation in 2D images
Author: Teney, D.
Piater, J.
Citation: Pattern Recognition, 2011 / Mester, R., Felsberg, M. (ed./s), vol.6835 LNCS, pp.336-345
Publisher: Springer
Issue Date: 2011
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783642231223
ISSN: 0302-9743
1611-3349
Conference Name: Joint Pattern Recognition Symposium (DAGM) (31 Aug 2011 - 02 Sep 2011 : Frankfurt am Main, Germany)
Statement of
Responsibility: 
Damien TeneyJustus Piater
Abstract: We present a novel way of performing pose estimation of known objects in 2D images. We follow a probabilistic approach for modeling objects and representing the observations. These object models are suited to various types of observable visual features, and are demonstrated here with edge segments. Even imperfect models, learned from single stereo views of objects, can be used to infer the maximumlikelihood pose of the object in a novel scene, using a Metropolis-Hastings MCMC algorithm, given a single, calibrated 2D view of the scene. The probabilistic approach does not require explicit model-to-scene correspondences, allowing the system to handle objects without individuallyidentifiable features. We demonstrate the suitability of these object models to pose estimation in 2D images through qualitative and quantitative evaluations, as we show that the pose of textureless objects can be recovered in scenes with clutter and occlusion.
Description: LNCS, volume 6835
Rights: © Springer-Verlag Berlin Heidelberg 2011
RMID: 0030043276
DOI: 10.1007/978-3-642-23123-0_34
Published version: http://dx.doi.org/10.1007/978-3-642-23123-0
Appears in Collections:Computer Science publications

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