Name
Title | ||
|---|---|---|
Estimating scene flow using an interconnected patch surface model with belief-propagation inference |
Abstract | ||
|---|---|---|
This article presents a novel method for estimating the dense three-dimensional motion of a scene from multiple cameras. Our method employs an interconnected patch model of the scene surfaces. The interconnected nature of the model means that we can incorporate prior knowledge about neighbouring scene motions through the use of a Markov Random Field, whilst the patch-based nature of the model allows the use of efficient techniques for estimating the local motion at each patch. An important aspect of our work is that the method takes account of the fact that local surface texture strongly dictates the accuracy of the motion that can be estimated at each patch. Even with simple squared-error cost functions, it produces results that are either equivalent to or better than results from a method based upon a state-of-the-art optical flow technique, which uses well-developed robust cost functions and energy minimisation techniques. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.cviu.2014.01.001 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
belief-propagation inference,novel method,patch-based nature,patch surface model,simple squared-error cost function,dense three-dimensional motion,estimating scene flow,patch model,local surface texture,robust cost function,scene surface,neighbouring scene motion,local motion,tracking,stereo,motion | Computer vision,Markov random field,Inference,Computer science,Flow (psychology),Minimisation (psychology),Artificial intelligence,Surface finish,Optical flow,Machine learning,Belief propagation | Journal |
Volume | Issue | ISSN |
121, | 1 | 1077-3142 |
Citations | PageRank | References |
0 | 0.34 | 36 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Popham | 1 | 6 | 3.89 |
| Abhir Bhalerao | 2 | 399 | 37.56 |
| Roland Wilson | 3 | 7 | 1.91 |