Title :
Motion estimation and segmentation method based on integration of spatial and temporal probability models
Author :
Linghu, Yong-Fang
Author_Institution :
Guizhou Univ. of Finance & Econ., Guiyang, China
Abstract :
A novel video motion object automatic segmentation algorithm based on a Bayesian framework is studied in this paper. A fast estimation procedure for the posterior marginals is added to the MAP algorithm.The field is initialized as the temporal segmentation result and the spatial segmentation is provided as an observed field of the image. Firstly, initial segmentation is applied to obtain number of the initial motions and the corresponding initial parameters of the motion model.Then the parameters are updated by using the given parameter estimation method. The experiment results show that the algorithm proposed is effective.
Keywords :
Bayes methods; image segmentation; maximum likelihood estimation; motion estimation; probability; spatiotemporal phenomena; video signal processing; Bayesian framework; MAP algorithm; motion estimation; motion segmentation; posterior marginals estimation procedure; spatial probability models; spatial segmentation; temporal probability models; temporal segmentation; video motion object automatic segmentation algorithm; Bayesian methods; Image motion analysis; Image segmentation; Motion estimation; Object segmentation; Optical sensors; Parameter estimation; Partitioning algorithms; Video compression; Videoconference; Bayesian framework; MAP algorithm; Maximizer of the posterior marginals; Spatio-temporal segmentation; video object;
Conference_Titel :
Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-3883-9
Electronic_ISBN :
978-1-4244-3884-6
DOI :
10.1109/ICASID.2009.5276983
