Name
Abstract | ||
|---|---|---|
Although stereo vision has made great progress in recent years, there are limited works which estimate the disparity for challenging scenes such as tunnel scenes. In such scenes, owing to the low light conditions and fast camera movement, the images are severely degraded by motion blur. These degraded images limit the performance of the standard stereo vision algorithms. To address this issue, in this paper, we combine the stereo vision with the image deblurring algorithms to improve the disparity result. The proposed algorithm consists of three phases: the PSF estimation phase; the image restoration phase; and the stereo vision phase. In the PSF estimation phase, we introduce three methods to estimate the blur kernel, which are optical flow based algorithm, cepstrum base algorithm and simple constant kernel algorithm, respectively. In the image restoration phase, we propose a fast non-blind image deblurring algorithm to recover the latent image. In the last phase, we propose a multi-scale multi-path Viterbi algorithm to compute the disparity given the deblurred images. The advantages of the proposed algorithm are demonstrated by the experiments with data sequences acquired in the tunnel. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006112304020409 | PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 6 |
Keywords | Field | DocType |
Stereo Vision, Image Deblurring, Optical Flow, Cepstrum | Computer vision,Stereo cameras,Stereopsis,Computer science,Artificial intelligence,Computer stereo vision | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuquan Xu | 1 | 16 | 4.95 |
| Seiichi Mita | 2 | 316 | 38.88 |
| Hossein Tehrani Niknejad | 3 | 112 | 7.29 |
| Kazuhisa Ishimaru | 4 | 7 | 2.86 |