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P. Bouthemy, "A Maximum Likelihood Framework for Determining Moving Edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 5, pp. 499511, May, 1989.  
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@article{ 10.1109/34.24782, author = {P. Bouthemy}, title = {A Maximum Likelihood Framework for Determining Moving Edges}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {11}, number = {5}, issn = {01628828}, year = {1989}, pages = {499511}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.24782}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Maximum Likelihood Framework for Determining Moving Edges IS  5 SN  01628828 SP499 EP511 EPD  499511 A1  P. Bouthemy, PY  1989 KW  picture processing; information extraction; maximum likelihood framework; moving edges; hypothesis testing; surface patch; 3D spatiotemporal space; spatiotemporal segmentation; occlusion contours; displacement magnitude; picture processing VL  11 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The determination of moving edges in an image sequence is discussed. An approach is proposed that relies on modeling principles and likely hypothesis testing techniques. A spatiotemporal edge in an image sequence is modeled as a surface patch in a 3D spatiotemporal space. A likelihood ratio test enables its detection as well as simultaneous estimation of its related attributes. It is shown that the computation of this test leads to convolving the image sequence with a set of predetermined masks. The emphasis is on a restricted but widely relevant and useful case of surface patch, namely the planar one. In addition, an implementation of the procedure whose computation cost is merely equivalent to a spatial gradient operator is presented. This method can be of interest for motionanalysis schemes, not only for supplying spatiotemporal segmentation, but also for extracting local motion information. Moreover, it can cope with occlusion contours and important displacement magnitude. Experiments have been carried out with both synthetic and real images.
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