DocumentCode
2041184
Title
Stochastic motion estimation and its applications
Author
Yung-Nien Sun ; Ming-Huwi Horng
Author_Institution
Inst. of Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Volume
2
fYear
1993
fDate
19-21 Oct. 1993
Firstpage
961
Abstract
Motion is an important clue used in human vision to extract objects of interest from background with irrelevant details. In image analysis, motion stems from the relative displacement between sensor and scene under observation. In this paper, a posteriori probabilistic approach is used to define this problem of the motion estimation. The motion vector is estimated by maximizing the a posteriori probability distribution of the relation intensity distributions.<>
Keywords
image segmentation; motion estimation; probability; human vision; image analysis; image segmentation; motion vector; object extraction; probabilistic approach; relation intensity distributions; scene; sensor; stochastic motion estimation; time varying images; Additive noise; Computer vision; Equations; Gaussian distribution; Image motion analysis; Image segmentation; Layout; Motion estimation; Probability distribution; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location
Beijing, China
Print_ISBN
0-7803-1233-3
Type
conf
DOI
10.1109/TENCON.1993.320173
Filename
320173
Link To Document