Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Human Body Pose Estimation Using Silhouette Shape Analysis
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Appearance Modeling Under Geometric Context
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Silhouette Shape Descriptors for Example-based Human Pose Recovery
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Shape context descriptors have been a valuable tool in shape description since their introduction. In this paper we examine the performance of shape context descriptors in the presence of noisy human silhouette data. Shape context descriptors have been shown to be robust to Gaussian noise in the task of shape matching. We implement four different configurations of shape context by altering the spacing of the histogram bins and then test the performance of these configurations in the presence of noise. The task used for these tests is recognition of body part shapes in human silhouettes. The noise in human silhouettes is principally from three sources: the noise from errors in silhouette segmentation, noise from loose clothing and noise from occlusions. We show that in the presence of this noise a newly proposed spacing for the shape context histogram bins has the best performance.