Abstract
Recently, a qualitative approach was proposed for 3-D shape recovery based on a hybrid object representation1. In this approach, aspect recovery is the most important stage which binds regions in the image into meaningful aspects to support 3-D primitive recovery. There is no known polynomial time algorithm to solve this problem. The previous approach dealt with this problem by using a heuristic method based on the conditional probability. Unlike the previous method, this paper presents a novel parallel voting scheme to conquer the problem for efficiency. For this purpose, the previous global aspect representation is replaced with a distributed representation of aspects. Based on this representation, a three-layer parallel voting network for aspect recovery is proposed. For evaluating likelihood, a continuous Hopfield net is employed so that all aspect coverings in decreasing order of likelihood can be enumerated. The paper describes this method in detail and demonstrates its usefulness with simulation. © 1995, Science Press, Beijing China and Allerton Press Inc.. All rights reserved.
| Original language | Undefined |
|---|---|
| Pages (from-to) | 385-402 |
| Number of pages | 18 |
| Journal | Journal of Computer Science and Technology |
| Volume | 10 |
| Issue number | 5 |
| Publication status | Published - Sept 1995 |
Keywords
- 3-D shape recovery
- Computer vision
- Hopfield net
- Parallel computing
- voting scheme