Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Allili, M. S.; Ziou, D.
Automatic colour-texture image segmentation using active contours Journal Article
In: International Journal of Computer Mathematics, vol. 84, no. 9, pp. 1325–1338, 2007, ISSN: 00207160.
Abstract | Links | BibTeX | Tags: Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures
@article{allili_automatic_2007,
title = {Automatic colour-texture image segmentation using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548354750&doi=10.1080%2f00207160701250501&partnerID=40&md5=69002e599b1c570571b04367ec08d2ac},
doi = {10.1080/00207160701250501},
issn = {00207160},
year = {2007},
date = {2007-01-01},
journal = {International Journal of Computer Mathematics},
volume = {84},
number = {9},
pages = {1325–1338},
abstract = {In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.},
keywords = {Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.
2.
Allili, M. S.; Ziou, D.
Globally adaptive region information for automatic color-texture image segmentation Journal Article
In: Pattern Recognition Letters, vol. 28, no. 15, pp. 1946–1956, 2007, ISSN: 01678655.
Abstract | Links | BibTeX | Tags: Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures
@article{allili_globally_2007,
title = {Globally adaptive region information for automatic color-texture image segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548675168&doi=10.1016%2fj.patrec.2007.05.002&partnerID=40&md5=e1338223b9cb99afc35dfbfbf7859b72},
doi = {10.1016/j.patrec.2007.05.002},
issn = {01678655},
year = {2007},
date = {2007-01-01},
journal = {Pattern Recognition Letters},
volume = {28},
number = {15},
pages = {1946–1956},
abstract = {In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.},
keywords = {Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.
3.
Allili, M. S.; Ziou, D.
Automatic color-texture image segmentation by using active contours Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4153 LNCS, pp. 495–504, 2006, ISSN: 03029743, (ISBN: 354037597X; 9783540375975 Place: Xi'an Publisher: Springer Verlag).
Abstract | Links | BibTeX | Tags: Active contours, Algorithms, Automatic segmentation, Automation, Boundary localization, Color texture segmentation, Color vision, Image segmentation, Information analysis, Textures
@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}
}
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.