Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
The nature of statistical learning theory
The nature of statistical learning theory
DiamondTouch: a multi-user touch technology
Proceedings of the 14th annual ACM symposium on User interface software and technology
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays
Proceedings of the 16th annual ACM symposium on User interface software and technology
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Visual tracking of bare fingers for interactive surfaces
Proceedings of the 17th annual ACM symposium on User interface software and technology
Cooperative gestures: multi-user gestural interactions for co-located groupware
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ICML '06 Proceedings of the 23rd international conference on Machine learning
wikiTable: finger driven interaction for collaborative knowledge-building workspaces
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
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This paper proposes a new approach to shape classification that is well suited to the specific challenges of vision-based hand posture recognition in a multi-user tabletop collaboration scenario. We use a representation of the 2-D hand silhouette where in-plane rotation and mirror symmetry appear as particular cases of permutations, and then show how to take advantage of this pattern to develop an efficient version of the permutation invariant SVM. Invariance to these transformations is very important because the users stand around the table, and a video camera captures the scene from the top. We also report experimental results that compare this approach favorably over common classification approaches, under the stated requirements.