Name
Abstract | ||
|---|---|---|
Depth acquisition for moving objects becomes increasingly critical for some applications such as human facial expression recognition. This paper presents a method for capturing the depth maps of moving objects that uses a one-shot black-and-white stripe pattern with the features of simplicity and easily generation. Considering the accuracy of a matching is crucial for a precise depth map but the matching of variant-width stripes is sparse and rough, the phase differences extracted by Gabor filter to achieve a pixel-wise matching with sub-pixel accuracy are used. The details of the derivation are presented to prove that this method based on the phase difference calculated by Gabor filter is valid. In addition, the periodic ambiguity of the encoded stripe is eliminated by the epipolar segment covering a given depth range at a camera-projector calibrating stage to decrease the calculation complexity. Experimental results show that our method can get a dense and accurate depth map of a moving object. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/VCIP.2013.6706402 | VCIP |
Keywords | Field | DocType |
phase differences,phase-difference based matching,image matching,pixel wise matching,face recognition,dense depth acquisition,gabor filters,precise depth map,calculation complexity,variant width stripes,periodic ambiguity,camera projector calibrating stage,gabor filter,moving objects,structured light,epipolar segment covering,human facial expression recognition,accurate depth map,one shot stripe structured light,subpixel accuracy,stripe pattern | Computer vision,Facial recognition system,Structured light,Epipolar geometry,Pattern recognition,Computer science,Gabor filter,Artificial intelligence,Depth map,Periodic graph (geometry),Ambiguity,Calibration | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4799-0288-0 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Li | 1 | 0 | 0.34 |
| Fu Li | 2 | 51 | 8.07 |
| Guangming Shi | 3 | 2663 | 184.81 |
| Fei Qi | 4 | 182 | 15.10 |
| Yuexin Shi | 5 | 0 | 0.34 |
| Shan Gao | 6 | 3 | 0.84 |