@inproceedings{allili_effective_2009,

title = {Effective object tracking by matching object and background models using active contours},

author = {M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951940408&doi=10.1109%2fICIP.2009.5414279&partnerID=40&md5=6838bb85dbef6c9548684a506df3d2b2},

doi = {10.1109/ICIP.2009.5414279},

isbn = {15224880 (ISSN); 978-142445654-3 (ISBN)},

year  = {2009},

date = {2009-01-01},

booktitle = {Proceedings - International Conference on Image Processing, ICIP},

pages = {873–876},

publisher = {IEEE Computer Society},

address = {Cairo},

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

note = {Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP},

keywords = {Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{allili_finite_2007,

title = {Finite generalized Gaussian mixture modeling and applications to image and video foreground segmentation},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548671710&doi=10.1109%2fCRV.2007.33&partnerID=40&md5=be89ffce30db18d0716df9eba2a197a2},

doi = {10.1109/CRV.2007.33},

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 = {183–190},

address = {Montreal, QC},

abstract = {In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the Maximum-Likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture. © 2007 IEEE.},

keywords = {Data structures, Finite mixture models, Foreground segmentation, Image segmentation, Information theory, Mathematical models, Maximum likelihood estimation, Mixture of General Gaussions (MoGG)},

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}

}

@inproceedings{allili_robust_2007,

title = {A robust video foreground segmentation by using generalized gaussian mixture modeling},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548763871&doi=10.1109%2fCRV.2007.7&partnerID=40&md5=e720d61af262e01677a3e23d8c4e6ad0},

doi = {10.1109/CRV.2007.7},

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 = {503–509},

address = {Montreal, QC},

abstract = {In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GDDS and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model. © 2007 IEEE.},

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

keywords = {Bayesian networks, Gaussian mixtures, Image segmentation, Mathematical models, Mixture of general gaussians (MoGG), MML, Video foreground segmentation, Video signal processing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{allili_robust_2006,

title = {A robust video object tracking by using active contours},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845513941&doi=10.1109%2fCVPRW.2006.20&partnerID=40&md5=64ff2be5c45a6c206420bf6eb5589bca},

doi = {10.1109/CVPRW.2006.20},

isbn = {0769526462 (ISBN); 978-076952646-1 (ISBN)},

year  = {2006},

date = {2006-01-01},

booktitle = {2006 Conference on Computer Vision and Pattern Recognition Workshops},

volume = {2006},

pages = {135},

publisher = {IEEE Computer Society},

address = {New York, NY},

abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region feature s distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences. © 2006 IEEE.},

note = {Journal Abbreviation: Conf. Comput. Vision Pattern Recog. Workshops},

keywords = {Boundary, Boundary localization, Color, Feature distribution, Image processing, Image segmentation, Kullback-Leibler distance, Level sets, Mathematical models, Mixture of pdfs, Object recognition, Object Tracking, Texture, Tracking (position), Variational techniques, Video object tracking},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bouchard_emg–torque_1999,

title = {EMG-to-torque dynamic relationship for elbow constant angle contractions},

author = {Stephane Bouchard and Denis Rancourt and Edward A. Clancy},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033353983&partnerID=40&md5=cf7a2b4c35a58c2409dda2d3f1af124d},

isbn = {05891019 (ISSN); 0780356756 (ISBN)},

year  = {1999},

date = {1999-01-01},

booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings},

volume = {1},

pages = {573},

publisher = {IEEE, Piscataway, NJ, United States},

address = {Atlanta, GA, USA},

abstract = {The purpose of this work was to determine the optimal EMG-torque relationship using four different EMG processors in conjunction with different system identification (ID) techniques for dynamically torque varying elbow constant angle contractions. Comparing predicted torque with actual elbow torque, it was found that either multiple EMG channels or EMG signal whitening lead to the best relationship. The choice of the system ID model had limited effect on performance.},

note = {Journal Abbreviation: Annu Int Conf IEEE Eng Med Biol Proc},

keywords = {Electromyography, Joints (anatomy), Mathematical models, Signal whitening, Torque},

pubstate = {published},

tppubtype = {inproceedings}

}