A parallel feature tracker for extended image sequences |
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Common terms and phrases
3-D feature points 3.1 Function minimization 6.1 Simulation results affine Anandan base image based tracker basis functions bilinear deformations Cal-cube sequence computer vision deformed spline control differential method displacement estimate diverging tree sequence feature tracking flow estimates Fuh and Maragos Hessian matrix image registration algorithm intensity error interframe motion interpolation Levenberg-Marquardt algorithm locally translational model long image sequences Matching all images minimum eigenvalues monotonicity operator monotonicity tracker motion estimates motion research Szeliski photogrammetry ray-traced sequence Rayshade registration example taxi Rehg and Witkin result of monotonicity RMS pixel error robust rotating tree sequence Section Shi and Tomasi Shi-Tomasi tracker shown in Figure spline control grid spline control vertices spline-based and Shi-Tomasi Spline-based image registration spline-based tracker structure from motion sub-pixel Szeliski and Coughlan Szeliski and Kang technique test our tracker Tomasi and Kanade top 50 tracks tracker are shown tracking features translating tree sequence tree 10 frames vector yosemite sequence
Bibliographic information
| Title | A parallel feature tracker for extended image sequences Cambridge Research Laboratory technical report series |
| Authors | Richard Szeliski, Sing Bing Kang, Heung-Yeung Shum |
| Publisher | Digital Equipment Corporation, Cambridge Research Laboratory, 1995 |
| Original from | Cornell University |
| Digitized | Sep 9, 2010 |
| Length | 24 pages |
| Subjects | › Computer vision Computers / Computer Vision & Pattern Recognition Image processing |
| Export Citation | BiBTeX EndNote RefMan |