Determination of the orientation of 3D objects using spherical harmonics
Graphical Models and Image Processing
Machine Learning
Digital Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast point feature histograms (FPFH) for 3D registration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Spectral-Driven Isometry-Invariant Matching of 3D Shapes
International Journal of Computer Vision
Hough transform and 3D SURF for robust three dimensional classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Unique signatures of histograms for local surface description
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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
Circular Blurred Shape Model for Multiclass Symbol Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Characterizing shape using conformal factors
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Multi-modal gesture recognition challenge 2013: dataset and results
Proceedings of the 15th ACM on International conference on multimodal interaction
Hi-index | 0.00 |
Hand pose recognition in advanced Human Computer Interaction systems (HCI) is becoming more feasible thanks to the use of affordable multi-modal RGB-Depth cameras. Depth data generated by these sensors is a very valuable input information, although the representation of 3D descriptors is still a critical step to obtain robust object representations. This paper presents an overview of different multi-modal descriptors, and provides a comparative study of two feature descriptors called Multi-modal Hand Shape (MHS) and Fourier-based Hand Shape (FHS), which compute local and global 2D-3D hand shape statistics to robustly describe hand poses. A new dataset of 38K hand poses has been created for real-time hand pose and gesture recognition, corresponding to five hand shape categories recorded from eight users. Experimental results show good performance of the fused MHS and FHS descriptors, improving recognition accuracy while assuring real-time computation in HCI scenarios.