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2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
New York, NY
June 17-June 22
ISBN: 0-7695-2597-0
ASCII Text
x 
 
Iasonas Kokkinos, Petros Maragos, Alan Yuille, "Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 2, pp. 1893-1900, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006.
 
 
BibTex
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@article{ 10.1109/CVPR.2006.74,
author = {Iasonas Kokkinos and Petros Maragos and Alan Yuille},
title = {Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models},
journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
volume = {2},
year = {2006},
issn = {1063-6919},
pages = {1893-1900},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2006.74},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
TI - Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
SN - 1063-6919
SP1893
EP1900
A1 - Iasonas Kokkinos,
A1 - Petros Maragos,
A1 - Alan Yuille,
PY - 2006
KW - null
VL - 2
JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
ER -
 
Iasonas Kokkinos, National Technical University of Athens
Petros Maragos, National Technical University of Athens
A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach.
Citation:
Iasonas Kokkinos, Petros Maragos, Alan Yuille, "Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models," cvpr, vol. 2, pp.1893-1900, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006
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