@inproceedings{allili_using_2007,

title = {Using feature selection for object segmentation and tracking},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548781938&doi=10.1109%2fCRV.2007.67&partnerID=40&md5=3fb26f3fcc7a6f55f705255758fef582},

doi = {10.1109/CRV.2007.67},

isbn = {0-7695-2786-8 978-0-7695-2786-4},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},

pages = {191–198},

address = {Montreal, QC},

abstract = {Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance was used. © 2007 IEEE.},

keywords = {Active contours, Algorithms, Feature extraction, Feature relevance, Image segmentation, Maximum likelihood, Mixture models, Negative examples, Object of interest (OOI), Optimization, Target tracking},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{allili_adaptive_2007,

title = {Adaptive appearance model for object contour tracking in videos},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548723968&doi=10.1109%2fCRV.2007.9&partnerID=40&md5=c10008163f7f45f743ef0dcb13444c72},

doi = {10.1109/CRV.2007.9},

isbn = {0769527868 (ISBN); 978-076952786-4 (ISBN)},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},

pages = {510–517},

address = {Montreal, QC},

abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of the object tracking is based on variational calculus, where an adaptive parametric mixture model is used for object features representation. The tracking is based on matching the object mixture models between successive frames of the sequence by using active contours while adapting the mixture model to varying object appearance changes due to illumination conditions and camera geometry. The implementation of the method is based on level set active contours which allow for automatic topology changes and stable numerical schemes. We validate our approach on examples of object tracking performed on real video sequences. © 2007 IEEE.},

note = {Journal Abbreviation: Proc. Fourth Can. Conf. Comput. Robot Vis.},

keywords = {Adaptive parametric mixture models, Adaptive systems, Boundary, Color, Geometry, Image communication systems, Level sets, Level-sets, Mathematical models, Mixture of pdfs, Object mixture modelss, Pattern matching, Shape, Target tracking, Texture, Tracking, Variational techniques, Video sequences, Video signal processing},

pubstate = {published},

tppubtype = {inproceedings}

}