DocumentCode
2684417
Title
Learned temporal models of image motion
Author
Yacob, Y. ; Davis, Larry
Author_Institution
Comput. Vision Lab., Maryland Univ., College Park, MD, USA
fYear
1998
fDate
4-7 Jan 1998
Firstpage
446
Lastpage
453
Abstract
An approach for learning and estimating temporal-flow models from image sequences is proposed. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are also developed for modeling the motion of regions in rigid and coordinated motion. The performance of these models is demonstrated on several long image sequences of rigid and articulated bodies in motion
Keywords
image sequences; learning (artificial intelligence); motion estimation; estimating; image motion; image sequences; learning; temporal-flow bases; temporal-flow models; Biological system modeling; Computer vision; Humans; Image sequences; Leg; Motion analysis; Motion control; Motion estimation; Principal component analysis; Tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision, 1998. Sixth International Conference on
Conference_Location
Bombay
Print_ISBN
81-7319-221-9
Type
conf
DOI
10.1109/ICCV.1998.710757
Filename
710757
Link To Document