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
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;
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
DOI :
10.1109/TENCON.1993.320173
