@article{allili_object_2009,

title = {Object contour tracking using foreground and background distribution matching},

author = {M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651256419&doi=10.1007%2f978-3-642-10268-4_111&partnerID=40&md5=0852d2cf799d98cff187d1b10b2e5c34},

doi = {10.1007/978-3-642-10268-4_111},

issn = {03029743},

year  = {2009},

date = {2009-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {5856 LNCS},

pages = {954–961},

abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked object and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. The object contour is tracked by assigning pixels in a way that maximizes the likelihood of the object versus the background. We implement the approach using an EM-like algorithm which evolves the object contour exactly to its boundaries and adapts the distribution parameters of the object and the background to data. © 2009 Springer-Verlag Berlin Heidelberg.},

note = {ISBN: 3642102670; 9783642102677 

Place: Guadalajara, Jalisco},

keywords = {Active contours, Computer applications, Computer vision, Distribution matching, Distribution parameters, Image matching, Object contour, Tracked objects},

pubstate = {published},

tppubtype = {article}

}

@article{alliliObjectContourTracking2009,

title = {Object contour tracking using foreground and background distribution matching},

author = {M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651256419&doi=10.1007%2f978-3-642-10268-4_111&partnerID=40&md5=0852d2cf799d98cff187d1b10b2e5c34},

doi = {10.1007/978-3-642-10268-4_111},

issn = {03029743},

year  = {2009},

date = {2009-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {5856 LNCS},

pages = {954–961},

abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked object and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. The object contour is tracked by assigning pixels in a way that maximizes the likelihood of the object versus the background. We implement the approach using an EM-like algorithm which evolves the object contour exactly to its boundaries and adapts the distribution parameters of the object and the background to data. © 2009 Springer-Verlag Berlin Heidelberg.},

note = {ISBN: 3642102670; 9783642102677 

Place: Guadalajara, Jalisco},

keywords = {Active contours, Computer applications, Computer vision, Distribution matching, Distribution parameters, Image matching, Object contour, Tracked objects},

pubstate = {published},

tppubtype = {article}

}