Name
Abstract | ||
|---|---|---|
We present a method for 3D object modeling and recognition which is robust to scale and illumination changes, and to viewpoint variations. The object model is derived from the local features extracted and tracked on an image sequence of the object. The recognition phase is based on an SVM classifier. We analyse in depth all the crucial steps of the method, and report very promising results on a dataset of 11 objects, that show how the method is also tolerant to occlusions and moderate scene clutter. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11861898_26 | DAGM-Symposium |
Keywords | Field | DocType |
object recognition,svm classifier,local approach,image sequence,illumination change,crucial step,object modeling,object model,recognition phase,promising result,moderate scene clutter,local feature | Computer vision,3D single-object recognition,Clutter,Occultation,Computer science,Object model,Image processing,Kalman filter,Artificial intelligence,Luminance,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
4174 | 0302-9743 | 3-540-44412-2 |
Citations | PageRank | References |
2 | 0.38 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabetta Delponte | 1 | 50 | 4.72 |
| Elise Arnaud | 2 | 126 | 10.05 |
| Francesca Odone | 3 | 411 | 45.90 |
| Alessandro Verri | 4 | 1754 | 190.73 |