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Efficient rotation invariant object detection using boosted random Ferns
dc.contributor.author | Villamizar Vergel, Michael Alejandro |
dc.contributor.author | Moreno-Noguer, Francesc |
dc.contributor.author | Andrade-Cetto, Juan |
dc.contributor.author | Sanfeliu Cortés, Alberto |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2010-11-23T18:56:24Z |
dc.date.available | 2010-11-23T18:56:24Z |
dc.date.created | 2010 |
dc.date.issued | 2010 |
dc.identifier.citation | Villamizar, M.A. [et al.]. Efficient rotation invariant object detection using boosted random Ferns. A: IEEE Conference on Computer Vision and Pattern Recognition. "IEEE Conference on Computer Vision and Pattern Recognition(2010)". San Francisco: 2010, p. 1038-1045. |
dc.identifier.uri | http://hdl.handle.net/2117/10385 |
dc.description.abstract | We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs), which we compute during a preprocessing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject.lcsh | Image processing |
dc.subject.other | image classification image recognition pattern recognition PARAULES AUTOR: object recognition |
dc.subject.other | boosting |
dc.title | Efficient rotation invariant object detection using boosted random Ferns |
dc.type | Conference report |
dc.subject.lemac | Imatges -- Processament |
dc.contributor.group | Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents |
dc.identifier.doi | 10.1109/CVPR.2010.5540104 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Pattern recognition::Image recognition::Pose estimation |
dc.relation.publisherversion | http://dx.doi.org/10.1109/CVPR.2010.5540104 |
dc.rights.access | Open Access |
local.identifier.drac | 4128300 |
dc.description.version | Postprint (published version) |
local.citation.author | Villamizar, M.A.; Moreno, F.; Andrade-Cetto, J.; Sanfeliu, A. |
local.citation.contributor | IEEE Conference on Computer Vision and Pattern Recognition |
local.citation.pubplace | San Francisco |
local.citation.publicationName | IEEE Conference on Computer Vision and Pattern Recognition(2010) |
local.citation.startingPage | 1038 |
local.citation.endingPage | 1045 |