Nonlinear Color Space and Spatiotemporal MRF for Hierarchical Segmentation of Face Features in Video

Liévin, M.; Luthon, Franck

doi:10.1109/tip.2003.818013

Cited by 74 publications

(51 citation statements)

References 33 publications

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“…The three color opponent models presented here, the CIELAB model (Fairchild, 2005), the LUX model proposed by Luthon et al (Lievin and Luthon, 2004;Luthon et al, 2010) and the CoLIP model proposed by Gouinaud et al (Gouinaud et al, 2011), follow these three fundamental steps.…”

Section: Color Vision Based Modelsmentioning

confidence: 93%

“…The LUX approach is based on the LIP framework too, and has also been built by the following steps of human color visual perception (Lievin and Luthon, 2004;Luthon et al, 2010). Cone intensities are represented by (R, G, B) channels and then the nonlinear response of the retinal stage is modeled by the isomorphism φ applied on each chromatic tone channel,…”

Section: Cielab Modelmentioning

confidence: 99%

“…Then we describe the segmentation method based on color multi-thresholding, mathematical morphology processing and adapted for color opponent models. We compare results on CIELAB model, which is the most known and used human vision based model, LUX (Logarithmic hUe eXtension) (Lievin and Luthon, 2004) model, and CoLIP (Color Logarihtmic Image Processing) (Gouinaud et al, 2011) model, two human vision based models derived from the LIP (Logarihtmic Image Processing) theory (Jourlin and Pinoli, 1988;2001) and the visual human color perception. …”

Section: Introductionmentioning

confidence: 99%

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Color Segmentation of MGG Colored Cytological Images Using Non Linear Opponent Color Spaces

Gouinaud

Leclerc

2013

Image Anal Stereol

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This paper presents a color image segmentation method for the quantification of viable cells from samples obtained after cytocentrifugation process and May Grunwald Giemsa (MGG) coloration and then observed by optical microscopy. The method is based on color multi-thresholding and mathematical morphology processing using color information on human visual system based models such as CIELAB model, LUX (Logarithmic hUe eXtension) model and CoLIP (Color Logarithmic Image Processing) model, a new human color vision based model also presented in this article. The results show that the CoLIP model, developed following each step of the human visual color perception, is particularly well adapted for this type of images.

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Section: Color Vision Based Modelsmentioning

confidence: 93%

Section: Cielab Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color Segmentation of MGG Colored Cytological Images Using Non Linear Opponent Color Spaces

Gouinaud

Leclerc

2013

Image Anal Stereol

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“…The white points correspond to pixels detected as mobile, informative of facial feature motion (especially lips). For more details about this application, the reader is referred to [21,22]. For comparison purpose, we show on Fig.…”

Section: Application To Motion Detectionmentioning

confidence: 99%

On the use of entropy power for threshold selection

2004

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This paper deals with an entropic approach as unsupervised thresholding technique for image processing, in order to extract a relevant binary information from noisy data. It is dedicated to situations where a signal of relatively high energy is localized in the image whereas the noise is spread over the entire frame. The method is based on the computation of the entropy power of the information source, as defined by Shannon. The threshold used for binarization is proportional to the entropic deviation of the observation source. The performance of the approach is illustrated by two classical image preprocessing tasks, namely motion detection and edge detection. The evaluation set contains both synthetic data and real-world image sequences.Résumé Cet article propose une approche entropique comme technique de seuillage automatique en traitement d'images, dans le but d'extraire une information binaire pertinenteà partir de données bruitées. La méthode est applicable dans le cas d'un signal de forteénergie localisé dans le plan image, tandis que le bruit est majoritaire et diffus. La technique est basée sur la notion de puissance entropique d'une source d'observations, telle que définie par Shannon. Le seuil de binarisation est propotionnelà l'écart-type entropique de la source. L'efficacité de la méthode est illustrée par deux applications classiques du traitement d'images: la détection de mouvement par les changements temporels et la détection de contours par les maxima du gradient spatial. L'évaluation des performances est réaliséeà la fois sur des données suynthétiques et sur des images réelles.

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“…For face and lip segmentation, we use the LU X color space defined in [3]. This color space is non linear with respect to the RGB color components and is little sensitive to lighting variations.…”

Section: Color Conversionmentioning

confidence: 99%

Liptracking and Mpeg4 Animation With Feedback Control

Beaumesnil¹,

Luthon²,

Chaumont

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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This article deals with facial segmentation and liptracking with feedback control by face model synthesis. On this topic, the search community is divided into two parts : analysis and synthesis. We want to use all the knowledge to create a global analysis/synthesis chain where the image analysis needs the 3D synthesis and conversely. As it happens, applications like face tracking or augmented reality need a rapid, robust and descriptive-enough solution.Our solution is based on a two step approach : the first step is a real-time facial segmentation with active contour models and the second step recovers a 3D-face model in order to extract more precise parameters to adjust the first step. The contribution of this paper is to couple two research fields for creating a real time application. The results obtained show rapid and robust performances which could be exploited in a more global real-time face tracking application.

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Nonlinear Color Space and Spatiotemporal MRF for Hierarchical Segmentation of Face Features in Video

Cited by 74 publications

References 33 publications

Color Segmentation of MGG Colored Cytological Images Using Non Linear Opponent Color Spaces

Color Segmentation of MGG Colored Cytological Images Using Non Linear Opponent Color Spaces

On the use of entropy power for threshold selection

Liptracking and Mpeg4 Animation With Feedback Control

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