@article{allili_automatic_2006,

title = {Automatic color-texture image segmentation by using active contours},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750065117&doi=10.1007%2f11821045_52&partnerID=40&md5=a2eb2582bd6d565ff0c64278e31112a1},

doi = {10.1007/11821045_52},

issn = {03029743},

year  = {2006},

date = {2006-01-01},

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

volume = {4153 LNCS},

pages = {495–504},

abstract = {In this paper, we propose a novel method for unsupervised color-texture segmentation. The approach aims at combining color and texture features and active contours to build a fully automatic segmentation algorithm. By fully automatic, we mean the steps of region initialization and calculation of the number of regions are performed automatically by the algorithm. Furthermore, the approach combines boundary and region information for accurate region boundary localization. We validate the approach by examples of synthetic and natural color-texture image segmentation. © Springer-Verlag Berlin Heidelberg 2006.},

note = {ISBN: 354037597X; 9783540375975 

Place: Xi'an 

Publisher: Springer Verlag},

keywords = {Active contours, Algorithms, Automatic segmentation, Automation, Boundary localization, Color texture segmentation, Color vision, Image segmentation, Information analysis, Textures},

pubstate = {published},

tppubtype = {article}

}

@article{allili_bayesian_2005,

title = {A Bayesian approach for weighting boundary and region information for segmentation},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646174288&doi=10.1007%2f11558484_59&partnerID=40&md5=e98b02fbcf69f8acda0b171ce009d5ff},

doi = {10.1007/11558484_59},

issn = {03029743},

year  = {2005},

date = {2005-01-01},

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

volume = {3708 LNCS},

pages = {468–475},

abstract = {Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images. © Springer-Verlag Berlin Heidelberg 2005.},

note = {ISBN: 354029032X; 9783540290322 

Place: Antwerp},

keywords = {Adaptive systems, Bayesian approach, Boundary localization, Boundary value problems, decision making, Image segmentation, Lighting, Parameter estimation, Segmentation, Textures, Variational image segmentation},

pubstate = {published},

tppubtype = {article}

}