Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108883
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dc.contributor.authorTeney, D.en
dc.contributor.authorPiater, J.en
dc.date.issued2013en
dc.identifier.citationProceedings of the 10th International Conference on Computer and Robot Vision, 2013 / pp.121-127en
dc.identifier.isbn9780769549835en
dc.identifier.urihttp://hdl.handle.net/2440/108883-
dc.description.abstractWe present a general method for tackling the related problems of pose estimation of known object instances and object categories. By representing the training images as a probability distribution over the joint appearance/pose space, the method is naturally suitable for modeling the appearance of a single instance of an object, or of diverse instances of the same category. The training data is weighted and forms a generative model, the weights being based on the informative power of each image feature for specific poses. Pose inference is performed through probabilistic voting in pose space, which is intrinsically robust to clutter and occlusions, and which we render tractable by treating separately the least interdependent dimensions. The scalability of category-level models is ensured during training by clustering the available image features in the joint appearance/pose space. Finally, we show how to first efficiently use a category-model, then possibly recognize a particular trained instance to refine the pose estimate using the corresponding instance-specific model. Our implementation uses edge points as image features, and was tested on several existing datasets. We obtain results on par with or superior to state-of-the-art methods, on both instance- and category-level problems, including for generalization to unseen instances.en
dc.description.statementofresponsibilityDamien Teney, Justus Piateren
dc.language.isoenen
dc.publisherIEEEen
dc.rights© 2013 IEEEen
dc.source.urihttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6568399en
dc.titleContinuous pose estimation in 2D images at instance and category levelsen
dc.typeConference paperen
dc.identifier.rmid0030043264en
dc.contributor.conference10th International Conference on Computer and Robot Vision (CRV) (29 May 2013 - 31 May 2013 : Regina, Canada)en
dc.identifier.doi10.1109/CRV.2013.34en
dc.identifier.pubid235800-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS07en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidTeney, D. [0000-0003-2130-6650]en
Appears in Collections:Computer Science publications

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