Name
Abstract | ||
|---|---|---|
The problem of object recognition has not yet been solved in its general form. The most successful approach to it so far relies on object models obtained by training a statistical method on visual features obtained from camera images. The images must necessarily come from huge visual datasets, in order to circumvent all problems related to changing illumination, point of view, etc. We hereby propose to also consider, in an object model, a simple model of how a human being would grasp that object (its affordance). This knowledge is represented as a function mapping visual features of an object to the kinematic features of a hand while grasping it. The function is practically enforced via regression on a human grasping database. After describing the database (which is publicly available) and the proposed method, we experimentally evaluate it, showing that a standard object classifier working on both sets of features (visual and motor) has a significantly better recognition rate than that of a visual-only classifier. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TAMD.2011.2106782 | IEEE T. Autonomous Mental Development |
Keywords | Field | DocType |
object affordances,object models,biologically inspired feature extraction,visual feature,function mapping visual feature,statistical analysis,robot vision systems,simple model,learning systems,camera images,huge visual datasets,object model,better recognition rate,knowledge representation,image sensors,feature extraction,standard object classifier,cameras,object recognition,visual-only classifier,statistical method,robot tactile systems,robot vision,improve object recognition,support vector machines,support vector machine,visualization | Computer vision,Viola–Jones object detection framework,3D single-object recognition,Method,Visualization,Computer science,Support vector machine,Object model,Feature extraction,Artificial intelligence,Machine learning,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
3 | 3 | 1943-0604 |
Citations | PageRank | References |
26 | 0.83 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Castellini | 1 | 448 | 33.56 |
| Tatiana Tommasi | 2 | 502 | 29.31 |
| N. Noceti | 3 | 30 | 1.91 |
| F. Odone | 4 | 98 | 10.27 |
| B. Caputo | 5 | 140 | 5.57 |