Name
Abstract | ||
|---|---|---|
This work proposes a compositional approach to hand posture recognition, using sparse features. The hand posture is decomposed into relevant compositions which are learned for each hand posture class without supervision; no hand segmentations or localization during training is needed. To learn relevant composition prototypes, an entropy range maximization loop was introduced, by performing k-means clustering several times. Experimental results compare favorably with results of both image categorization and hand posture recognition reported in literature. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/SACI.2009.5136287 | Timisoara |
Keywords | Field | DocType |
edge detection,entropy,gesture recognition,learning (artificial intelligence),pattern clustering,compositional technique,edge detection,entropy range maximization loop,hand posture recognition,image categorization,k-means clustering,machine learning,sparse feature | k-means clustering,Categorization,Computer vision,Histogram,Computational intelligence,Pattern recognition,Computer science,Gesture recognition,Image segmentation,Feature extraction,Artificial intelligence,Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-1-4244-4478-6 | 0 | 0.34 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simion Georgiana | 1 | 2 | 1.39 |
| Vasile Gui | 2 | 27 | 4.34 |
| Marius Otesteanu | 3 | 10 | 5.29 |