Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding

Representing and modeling the motion and spatial support of multiple objects and surfaces from motion video sequences is an important intermediate step towards dynamic image understanding. One such representation, called layered representation, has recently been proposed. Although a number of algori...

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Bibliographic Details
Published inProceedings of IEEE International Conference on Computer Vision pp. 777 - 784
Main Authors Ayer, S., Sawhney, H.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1995
Subjects
Helium
Image coding
Laboratories
Maximum likelihood estimation
Motion estimation
Robustness
Signal processing algorithms
Video compression
Video sequences
Video signal processing
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Summary:Representing and modeling the motion and spatial support of multiple objects and surfaces from motion video sequences is an important intermediate step towards dynamic image understanding. One such representation, called layered representation, has recently been proposed. Although a number of algorithms have been developed for computing these representations, there has not been a consolidated effort into developing a precise mathematical formulation of the problem. This paper presents one such formulation based on maximum likelihood estimation (MLE) of mixture models and the minimum description length (MDL) encoding principle. The three major issues in layered motion representation are: (i) how many motion models adequately describe image motion, (ii) what are the motion model parameters, and (iii) what is the spatial support layer for each motion model.< >
ISBN:9780818670428
0818670428
DOI:10.1109/ICCV.1995.466859