DMCA
Shape and motion from image streams under orthography: a factorization method (1992)
Cached
Download Links
| Venue: | INTERNATIONAL JOURNAL OF COMPUTER VISION |
| Citations: | 1094 - 38 self |
BibTeX
@ARTICLE{Tomasi92shapeand,
author = {Carlo Tomasi and Takeo Kanade},
title = {Shape and motion from image streams under orthography: a factorization method},
journal = {INTERNATIONAL JOURNAL OF COMPUTER VISION},
year = {1992},
volume = {9},
number = {2},
pages = {137--154}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Inferring scene geometry and camera motion from a stream of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. We have developed a factorization method that can overcome this difficulty by recovering shape and motion under orthography without computing depth as an intermediate step. An image stream can be represented by the 2FxP measurement matrix of the image coordinates of P points tracked through F frames. We show that under orthographic projection this matrix is of rank 3. Based on this observation, the factorization method uses the singular-value decomposition technique to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively. Two of the three translation components are computed in a preprocessing stage. The method can also handle and obtain a full solution from a partially filled-in measurement matrix that may result from occlusions or tracking failures. The method gives accurate results, and does not introduce smoothing in either shape or motion. We demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions.
Keyphrases
factorization method image stream measurement matrix camera rotation image coordinate full solution scene geometry intermediate step camera motion preprocessing stage outdoor image stream orthographic projection accurate result filled-in measurement matrix translation component ill-conditioned problem object shape singular-value decomposition technique
