Name
Abstract | ||
|---|---|---|
In this paper, we describe the components of a novel algorithm for the detection of grasping points from monocular images of previously unseen objects. A basic building block of our approach is the use of a newly devised descriptor, capable of representing grasping point shape and appearance by the use of histograms of oriented gradients in a semi-local manner. Combined with boosting our method learns discriminative grasp point models for new objects from a set of annotated real-world images. The method has been extensively evaluated on challenging images of real scenes, exhibiting largely varying characteristics concerning illumination conditions, scene complexity, and viewpoint. Our experiments show that the method, despite these variations, works in a stable manner and that its performance compares favorably to the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13772-3_21 | ICIAR |
Keywords | Field | DocType |
illumination condition,point detection,basic building block,discriminative grasp point model,oriented gradient,annotated real-world image,semi-local manner,monocular image,point shape,novel algorithm,stable manner,new object | Object detection,Computer vision,Histogram,GRASP,Pattern recognition,Computer science,Image based,Boosting (machine learning),Artificial intelligence,Monocular,Discriminative model,Shape context | Conference |
Volume | ISSN | ISBN |
6111 | 0302-9743 | 3-642-13771-7 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonidas Lefakis | 1 | 21 | 3.86 |
| Horst Wildenauer | 2 | 126 | 12.81 |
| Manuel Pascual Garcia-Tubio | 3 | 0 | 1.01 |
| Lech Szumilas | 4 | 9 | 3.64 |