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B.G. Schunck, "Image Flow Segmentation and Estimation by Constraint Line Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 10, pp. 10101027, October, 1989.  
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@article{ 10.1109/34.42834, author = {B.G. Schunck}, title = {Image Flow Segmentation and Estimation by Constraint Line Clustering}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {11}, number = {10}, issn = {01628828}, year = {1989}, pages = {10101027}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.42834}, 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  Image Flow Segmentation and Estimation by Constraint Line Clustering IS  10 SN  01628828 SP1010 EP1027 EPD  10101027 A1  B.G. Schunck, PY  1989 KW  computerised picture processing; pattern recognition; segmentation; line clustering; image flow velocity field; statistical test; smoothing algorithm; surface reconstruction; computerised pattern recognition; computerised picture processing; statistical analysis VL  11 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Image flow is the velocity field in the image plane caused by the motion of the observer, objects in the scene, or apparent motion, and can contain discontinuities due to object occlusion in the scene. An algorithm that can estimate the image flow velocity field when there are discontinuities due to occlusions is described. The constraint line clustering algorithm uses a statistical test to estimate the image flow velocity field in the presence of step discontinuities in the image irradiance or velocity field. Particular emphasis is placed on motion estimation and segmentation in situations such as random dot patterns where motion is the only cue to segmentation. Experimental results on a demanding synthetic test case and a real image are presented. A smoothing algorithm for improving the velocity field estimate is also described. The smoothing algorithm constructs a smooth estimate of the velocity field by approximating a surface between step discontinuities. It is noted that the velocity field estimate can be improved using surface reconstruction between velocity field boundaries.
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