Name
Abstract | ||
|---|---|---|
In hand shape recognition, transformation invariance is key for successful recognition. We propose a system that is invariant to small scale, translation and shape variations. This is achieved by using a-priori knowledge to create a transformation subspace for each hand shape. Transformation subspaces are created by performing Principal Component Analysis (PCA) on images produced using computer animation. A method to increase the efficiency of the system is outlined. This is achieved using a technique of grouping subspaces based on their origin and then organising them into a hierarchical decision tree. We compare the accuracy of this technique with that of the Tangent Distance technique and display the results. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.1134 | Pattern Recognition, 2006. ICPR 2006. 18th International Conference |
Keywords | Field | DocType |
computer animation,decision trees,object recognition,principal component analysis,computer animation,hand shape recognition,hierarchical decision tree,principal component analysis,tangent distance technique,transformation invariance,transformation subspace | Decision tree,Computer vision,Pattern recognition,Subspace topology,Invariant (physics),Computer science,Linear subspace,Invariant (mathematics),Artificial intelligence,Computer animation,Principal component analysis,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-7695-2521-0 |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Coogan | 1 | 1 | 0.36 |
| Alistair Sutherland | 2 | 101 | 14.36 |