Robotics and Computer Vision Laboratory Login  
  Robotics and Computer Vision Laboratory kaist logo
Archive Courses

Publications
Home  >  Research  >  Publications robust model-ba

 
[International Conference] Robust Model-based 3D Object Recognition by Combining Feature Matching with Tracking
Sungho Kim , In So Kweon , Incheol Kim
IEEE International Conference on Robotics and Automation (ICRA) , September 2003
Download
  ICRA2003_shkim.pdf ICRA2003_shkim.pdf (1,007.7K) [52]
Abstract
We propose a vision based 3D object recognition and tracking system, which provides high level scene descriptions such as object identification and 3D pose information. The system is composed of object recognition part and real-time tracking part. In object recognition, we propose a feature which is robust to scale, rotation, illumination change and background clutter. A probabilistic voting scheme maximizes the conditional probability defined by the features in correspondence to recognize an object of interest. As a result of object recognition, we obtain the homography between the model image and the input scene. In tracking, a Lie group formalism is used to cast the motion computation problem into simple geometric terms so that tracking becomes a simple optimization problem. An initial object pose is estimated using correspondences between the model image and the 3D CAD model which are predefined and the homography which relates the model image to the input scene. Results from the experiments show the robustness of the proposed system.

 
 
 

Robotics and Computer Vision Laboratory
KAIST | Electrical Engineering | Contact Us | Sitemap