Výsledky vyhledávání - "Marinette Revenu"

Artificial Intelligence and Pattern Recognition, Vision, Learning

Autor: Marinette Revenu, Régis Clouard, Olivier Sigaud, Isabelle Bloch

Publikováno v: A Guided Tour of Artificial Intelligence Research ISBN: 9783030061692

This chapter describes a few problems and methods combining artificial intelligence, pattern recognition, computer vision and learning. The intersection between these domains is growing and gaining importance, as illustrated in this chapter with a fe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::73fcefe45df95b0f58bd0a8bf562d7f2
https://doi.org/10.1007/978-3-030-06170-8_10

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AN ONTOLOGY-BASED MODEL FOR REPRESENTING IMAGE PROCESSING APPLICATION OBJECTIVES

Autor: Marinette Revenu, Arnaud Renouf, Régis Clouard

Publikováno v: International Journal of Pattern Recognition and Artificial Intelligence. 24:1181-1208

This paper investigates the types of information that are necessary and sufficient to design and evaluate image processing software programs, and proposes a representation of these information elements using a computational language performable by vi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6e006d42b175b39d8bbff054764ae01c
https://doi.org/10.1142/s0218001410008354

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Graph-based skin lesion segmentation of multispectral dermoscopic images

Autor: Olivier Lezoray, Michel Desvignes, Marinette Revenu

Publikováno v: ICIP
ICIP 2014-21st IEEE International Conference on Image Processing
ICIP 2014-21st IEEE International Conference on Image Processing, Oct 2014, Paris, France

International audience; Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. We present a novel method to detect skin lesion borders in multispectral der-moscopy images. First, hairs are detected on i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd58316d053585b9e5b5d66a8309a359
https://doi.org/10.1109/icip.2014.7025180

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New data model for graph-cut segmentation: Application to automatic melanoma delineation

Autor: Razmig Kéchichian, Marinette Revenu, Hao Gong, Olivier Lezoray, Michel Desvignes

Publikováno v: ICIP
ICIP 2014-21st IEEE International Conference on Image Processing
ICIP 2014-21st IEEE International Conference on Image Processing, Oct 2014, Paris, France

International audience; We propose a new data model for graph-cut image segmentation, defined according to probabilities learned by a classification process. Unlike traditional graph-cut methods, the data model takes into account not only color but a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9cecc211d2979bb4ed069a8f497862d3
https://doi.org/10.1109/icip.2014.7025179

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Visible and Infrared Face Identification via Sparse Representation

Autor: Pierre Buyssens, Marinette Revenu

Publikováno v: ISRN Machine Vision
ISRN Machine Vision, 2013, 2013, 10 p. ⟨10.1155/2013/579126⟩

International audience; We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb0d521f527d76d4551088b3689e4807
https://hal.archives-ouvertes.fr/hal-00829451

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Label Diffusion on Graph for Face Identification

Autor: Pierre Buyssens, Marinette Revenu

Publikováno v: ICB
ICB, Mar 2012, New Delhi, India. pp.46-51, ⟨10.1109/ICB.2012.6199757⟩

International audience; We present a generic classification method based on a label diffusion on graph. Inspired by the watershed transform widely used in the image segmentation field, we propose a simple algorithm working on distances graphs of any

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a136ee69b08e7fcbe33e2f000c28dc96
https://hal.archives-ouvertes.fr/hal-00805790/document

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Human-computer interaction for the generation of image processing applications

Autor: Arnaud Renouf, Régis Clouard, Marinette Revenu

Publikováno v: International Journal of Human-Computer Studies
International Journal of Human-Computer Studies, Elsevier, 2011, 69 (4), pp.201-219. ⟨10.1016/j.ijhcs.2010.12.002⟩

International audience; The development of customized image processing applications is time consuming and requires high level skills. This paper describes the design of an interactive application generation system oriented towards producing image pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ff59b23ada0044ad5652d434916f777
https://hal.archives-ouvertes.fr/hal-00805465/document

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Fusion levels of visible and infrared modalities for face recognition

Autor: Pierre Buyssens, Marinette Revenu

Publikováno v: BTAS

We present a study on different levels of visible and infrared modalities fusion for face recognition. While visible modality is the most natural way to recognize someone, infrared presents thermal distribution that can be useful for face recognition

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0a595ffd783d8102e037d1a81eff9830
https://doi.org/10.1109/btas.2010.5634542

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IR and Visible Face Identification via Sparse Representation

Autor: Marinette Revenu, Pierre Buyssens

Publikováno v: BTAS
BTAS, Sep 2010, Washington, United States. 6 p

International audience; We present a face recognition technique based on the sparsity principle. Parsimony is used both to compute the face feature vectors and to process the classification of these vectors. Applied to visible and infrared modalities

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbb8afd4cfcc299940a20e0fbff7c71c
https://hal.archives-ouvertes.fr/hal-00805773

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Learning Sparse Face Features : Application to Face Verification

Autor: Marinette Revenu, Pierre Buyssens

Publikováno v: ICPR
ICPR, Aug 2010, Istanbul, Turkey. pp.670-673, ⟨10.1109/ICPR.2010.169⟩

International audience; We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference per subject image. In classical feature-based approaches, a first stage

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf3d0b7a3c70b4b43502c412aa1aa55b
https://hal.archives-ouvertes.fr/hal-00805785/document

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