A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Skeleton Pruning by Contour Partitioning with Discrete Curve Evolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Connectivity-Based Skeleton Extraction in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Accurate 3D pose estimation from a single depth image
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A data-driven approach for real-time full body pose reconstruction from a depth camera
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Efficient regression of general-activity human poses from depth images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The current Kinect system extracts human skeletons from depth images by supervised learning methods, which require a large number of marker-based motion capture data for training. However, with limited budget, motion capture devices are not available and motion capture data is difficult to collect. In this paper, we propose a unsupervised skeletonization method to extract human skeletons from depth images without any training data. It considers the symmetry of skeletons to object boundary. The boundaries of human body are first formed from the edges involved from the characteristic changes in depth, then boundary analysis is performed to identify boundaries with different types, next a two-step skeletonization scheme is adopted to compute skeletons from different types of boundaries separately, finally skeletons generated from the two steps are combined as the output. Experimental results show that without any markers for training, human skeletons obtained by the proposed method can represent both the body parts without occlusion and the main torso under occlusion.