

de Recherche et d’Innovation
en Cybersécurité et Société
Allili, M. S.; Ziou, D.
Likelihood-based feature relevance for figure-ground segmentation in images and videos Article de journal
Dans: Neurocomputing, vol. 167, p. 658–670, 2015, ISSN: 09252312, (Publisher: Elsevier).
Résumé | Liens | BibTeX | Étiquettes: accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording
@article{allili_likelihood-based_2015,
title = {Likelihood-based feature relevance for figure-ground segmentation in images and videos},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952631642&doi=10.1016%2fj.neucom.2015.04.015&partnerID=40&md5=833948d0784e0dc42c2245b9343971dd},
doi = {10.1016/j.neucom.2015.04.015},
issn = {09252312},
year = {2015},
date = {2015-01-01},
journal = {Neurocomputing},
volume = {167},
pages = {658–670},
abstract = {We propose an efficient method for image/video figure-ground segmentation using feature relevance (FR) and active contours. Given a set of positive and negative examples of a specific foreground (an object of interest (OOI) in an image or a tracked objet in a video), we first learn the foreground distribution model and its characteristic features that best discriminate it from its contextual background. For this goal, an objective function based on feature likelihood ratio is proposed for supervised FR computation. FR is then incorporated in foreground segmentation of new images and videos using level sets and energy minimization. We show the effectiveness of our approach on several examples of image/video figure-ground segmentation. © 2015 Elsevier B.V.},
note = {Publisher: Elsevier},
keywords = {accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.; Ziou, D.
An automatic segmentation combining mixture analysis and adaptive region information: A level set approach Article d'actes
Dans: Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005, p. 73–80, Institute of Electrical and Electronics Engineers Inc., Genova, 2005, ISBN: 0769523196 (ISBN); 978-076952319-4 (ISBN), (Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV).
Résumé | Liens | BibTeX | Étiquettes: Adaptive segmentation, Automatic segmentations, Color image processing, Color image segmentation, Computer vision, Energy functionals, Image segmentation, Level Set, Level sets, Mixture analysis, Mixtures, Polarity smoothing, Posterior probability
@inproceedings{allili_automatic_2005-1,
title = {An automatic segmentation combining mixture analysis and adaptive region information: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845531999&doi=10.1109%2fCRV.2005.14&partnerID=40&md5=c9773a2f28fe00b4171511895b721158},
doi = {10.1109/CRV.2005.14},
isbn = {0769523196 (ISBN); 978-076952319-4 (ISBN)},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005},
volume = {1},
pages = {73–80},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
address = {Genova},
abstract = {In this paper, we propose a novel automatic framework for variational color image segmentation based on unifying adaptive region information and mixture modelling. We consider a formulation of the region information by using the posterior probability of a mixture of general Gaussian (GG) pdfs, where each region is represented by a pdf. The segmentation is formulated by the minimization of an energy functional according to the region contours and all the mixture parameters respectively. Two main objectives are achieved by the approach. A scheme is provided to extend easily the adaptive segmentation to an arbitrary number of regions and to perform it in a fully automatic fashion. Moreover, the segmentation recovers an accurate and representative mixture of pdfs. In the approach, we couple the boundary and region information of the image to steer the segmentation. We validate the method on the segmentation of real world color images. © 2005 IEEE.},
note = {Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV},
keywords = {Adaptive segmentation, Automatic segmentations, Color image processing, Color image segmentation, Computer vision, Energy functionals, Image segmentation, Level Set, Level sets, Mixture analysis, Mixtures, Polarity smoothing, Posterior probability},
pubstate = {published},
tppubtype = {inproceedings}
}