Pictorial Structures for Object Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Trees: Common-Factor Models for 2D Human Pose Recovery
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering Human Body Configurations Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multi-Aspect Detection of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images
International Journal of Computer Vision
Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle Detection in Aerial Imagery
International Journal of Computer Vision
Appearance sharing for collective human pose estimation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We address the problem of detecting human figures in images, taking into account that the image of the human figure may be taken from a range of viewpoints. We capture the geometric deformations of the 2D human figure using an extension of the Common Factor Model (CFM) of Lan and Huttenlocher. The key contribution of the paper is an improved iterative message passing inference algorithm that runs faster than the original CFM algorithm. This is based on the insight that messages created using the distance transform are shift invariant and therefore messages can be created once and then shifted for subsequent iterations. Since shifting (O(1) complexity) is faster than computing a distance transform (O(n) complexity), a significant speedup is observed in the experiments. We demonstrate the effectiveness of the new model for the human parsing problem using the Iterative Parsing data set and results are competitive with the state of the art detection algorithm of Andriluka, et al.