Abstract
In this paper, we address a new algorithm for recognition and reconstruction of 3-D polyhedral objects, based on perceptual grouping and graph search technique. Perceptual grouping is performed in a model-based framework, in which decision tree classifier is employed for learning and retrieving geometric information of the 3-D model object. On the other hand, in order to extract the polygonal patch structure, initial grouping result is represented by a Gestalt graph. Polygonal patch hypotheses are then generated by graph search and verified by the consistency test with the model. In the experiments, it is shown that the model-based grouping reduces the number of the generated hypotheses efficiently, and furthermore, robust recognition and reconstruction are achieved by means of the graph search technique.
| Original language | English |
|---|---|
| Pages (from-to) | 720-724 |
| Number of pages | 5 |
| Journal | Proceedings - International Conference on Pattern Recognition |
| Volume | 15 |
| Issue number | 1 |
| State | Published - 2000 |
| Externally published | Yes |
Bibliographical note
Funding Information:*This work was supported by the Agency for Defence Development (ADD), Taejon, Korea, and the Automatic Control Research Center (ACRC) in Seoul National University, Seoul, Korea.