Title :
Temporal surface reconstruction
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA, USA
Abstract :
An approach which allows an arbitrary structure estimation to be embedded into a recursive estimation process that incrementally improves a structure estimate with every new frame that becomes available is discussed. The approach is based on Bayesian estimation theory and the Kalman filter. The authors demonstrate how it may be applied to such domains as depth from motion and depth from shading
Keywords :
Kalman filters; computer vision; Bayesian estimation theory; Kalman filter; depth from motion; depth from shading; recursive estimation process; structure estimate; temporal surface reconstruction; Artificial intelligence; Contracts; Image reconstruction; Kalman filters; Layout; Motion estimation; Recursive estimation; Stereo image processing; Surface reconstruction; Uncertainty;
Conference_Titel :
Computer Vision and Pattern Recognition, 1991. Proceedings CVPR '91., IEEE Computer Society Conference on
Conference_Location :
Maui, HI
Print_ISBN :
0-8186-2148-6
DOI :
10.1109/CVPR.1991.139761
