Name
Abstract | ||
|---|---|---|
Many industrial applications involve high-speed translational motions. Classical translation analysis methods like region-matching break down when large motion velocity causes motion blur in the image. In this paper, we rely on geometric moments to realize the spatial integration which may exist in the ''motion-from-blur'' mechanism of the biological vision, derive a theorem for the relationship between the geometric moments of the motion blurred image and the translational motion, and develop a novel motion-from-blur method based on this theorem for estimating 2D high-speed translation from motion blurred images. This approach utilizes the ''motion blur'' cue rather than neglect it, and therefore can compute the velocity and the acceleration of the motion from only two successive frames of motion blurred images while existing approaches estimate an accelerated motion from at least three images. Furthermore, this algorithm decomposes the 2D translation analysis problem into two 1D parallel problems to improve the real-time performance. Experimental results with both uniform motion and accelerated motion show that this method achieves good accuracy and efficiency. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.patrec.2005.05.002 | Pattern Recognition Letters |
Keywords | Field | DocType |
accelerated motion show,large motion velocity,uniform motion,geometric moment,accelerated motion,motion blur,high-speed translation,motion blurred image,translational motion,high-speed translational motion,biological vision,moments,classical translation analysis method,motion estimation,real time | Structure from motion,Computer vision,Quarter-pixel motion,Motion field,Motion blur,Artificial intelligence,Acceleration,Motion estimation,Velocity Moments,Match moving,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 15 | Pattern Recognition Letters |
Citations | PageRank | References |
3 | 0.45 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baiqing Guan | 1 | 6 | 0.89 |
| Wang Shigang | 2 | 44 | 4.89 |
| Guobao Wang | 3 | 86 | 12.68 |