Guidelines for clinical trials in melasma, British Journal of Dermatology, vol.77, issue.s1, 2006. ,
DOI : 10.1016/j.jaad.2006.01.049
Introduction to the theory of statistics, 1974. ,
Reliability assessment and validation of the Melasma Area and Severity Index (MASI) and a new modified MASI scoring method, Journal of the American Academy of Dermatology, vol.64, issue.1, pp.78-83, 2011. ,
DOI : 10.1016/j.jaad.2009.10.051
Development and validation of a health-related quality of life instrument for women with melasma, British Journal of Dermatology, vol.149, p.572577, 2003. ,
Erbium:YAG Laser Resurfacing for Refractory Melasma, Dermatologic Surgery, vol.25, issue.2, p.121123, 1999. ,
DOI : 10.1046/j.1524-4725.1999.08103.x
Ecacy of glycolic acid peels in the treatment of melasma, Archives of Dermatology, vol.138, p.15781582, 2002. ,
Topical retinoic acid (tretinoin) for melasma in black patients. a vehiclecontrolled clinical trial, Arch Dermatol, vol.130, p.72733, 1994. ,
Non-Invasive Measurements of Skin Pigmentation In Situ, Pigment Cell Research, vol.4959, issue.6, p.618626, 2004. ,
DOI : 10.1016/S1076-0512(97)00097-6
In vivo measurement of skin erythema and pigmentation: new means of implementation of diuse reectance spectroscopy with a commercial instrument, British Journal of Dermatology, vol.159, p.683690, 2008. ,
Multi-spectral image analysis for skin pigmentation classication, Proc. IEEE International Conference on Image Processing (ICIP), 2010. ,
Classication of skin hyper-pigmentation lesions with multi-spectral images, 2012. ,
Intensity-Based Image Registration by Minimizing Residual Complexity, IEEE Transactions on Medical Imaging, vol.29, issue.11, p.18821891, 2010. ,
DOI : 10.1109/TMI.2010.2053043
A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing, vol.26, issue.1, 1988. ,
DOI : 10.1109/36.3001
Hyperspectral pixel unmixing via spectral band selection and dc-insensitive singular value decomposition. Geoscience and Remote Sensing Letters, 2007. ,
High-Order Contrasts for Independent Component Analysis, Neural Computation, vol.140, issue.1, p.157192, 1999. ,
DOI : 10.1109/78.599941
Fast and robust xed-point algorithms for independent component analysis, IEEE Trans. on Neural Networks, vol.10, p.626634, 1999. ,
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.18, issue.2, p.111126, 1994. ,
DOI : 10.1002/env.3170050203
Generalized nonnegative matrix approximations with bregman divergences, NIPS, 2005. ,
Learning the parts of objects by non-negative matrix factorization, p.788, 1999. ,
Fast autonomous spectral end-member determination in hyperspectral data, Conf. on Applied Geologic Remote Sensing, p.337344, 1999. ,
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery, IEEE Transactions on Signal Processing, vol.57, issue.11, p.43554368, 2009. ,
DOI : 10.1109/TSP.2009.2025797
URL : https://hal.archives-ouvertes.fr/hal-00548758
Random n-nder (n-ndr) endmember extraction algorithms for hyperspectral imagery, IEEE Trans. on Image Processing, vol.20, 2011. ,
Mapping target signatures via partial unmixing of aviris data, Summaries of JPL Airborne Earth Science Workshop, 1995. ,
A fast iterative algorithm for implementation of pixel purity index ,
Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.4, p.898910, 2005. ,
DOI : 10.1109/TGRS.2005.844293
Signal Theory Methods in Multispectral Remote Sensing, 2003. ,
DOI : 10.1002/0471723800
Feature extraction based on decision boundaries, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.4, p.388400, 1993. ,
DOI : 10.1109/34.206958
Decision boundary feature extraction for neural networks, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, p.7583, 1997. ,
DOI : 10.1109/ICSMC.1992.271652
Supervised band selection for optimal use of data from airborne hyperspectral sensors, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2003. ,
DOI : 10.1109/IGARSS.2003.1294245
Classication of skin hyper-pigmentation lesions with multi-spectral images, 2012. ,
Change detection techniques, International Journal of Remote Sensing, vol.66, issue.12, 2004. ,
DOI : 10.1659/0276-4741(2001)021[0175:LCCATA]2.0.CO;2
Image change detection algorithms: a systematic survey, IEEE Transactions on Image Processing, vol.14, issue.3, pp.294-307, 2005. ,
DOI : 10.1109/TIP.2004.838698
Illumination independent change detection for real world image sequences, Comput. Vis. Graph. Image Process, vol.46, p.387399, 1989. ,
Statistical change detection with moments under time-varying illumination, IEEE Trans. Image Process, vol.7, p.12581268, 1998. ,
Integrating intensity and texture dierences for robust change detection, IEEE Trans. Image Process, vol.11, p.105112, 2002. ,
An Introduction to Signal Detection and Estimation, 1994. ,
Statistical model-based change detection in moving video, Signal Processing, vol.31, issue.2, p.165180, 1993. ,
DOI : 10.1016/0165-1684(93)90063-G
Change detection techniques for ERS-1 SAR data, IEEE Transactions on Geoscience and Remote Sensing, vol.31, issue.4, p.896906, 1993. ,
DOI : 10.1109/36.239913
An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images, IEEE Transactions on Image Processing, vol.11, issue.4, p.452466, 2002. ,
DOI : 10.1109/TIP.2002.999678
A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.5, p.14321445, 2007. ,
DOI : 10.1109/TGRS.2007.893568
URL : https://hal.archives-ouvertes.fr/hal-00582539
Boundary and object detection in real world images, J. ACM, vol.23, p.599618, 1976. ,
Statistical Parametric Mapping: The Analysis of Functional Brain Images, 2007. ,
The Geometry of Random Fields, 1981. ,
Combining Spatial Extent and Peak Intensity to Test for Activations in Functional Imaging, Inria RESEARCH CENTRE SOPHIA ANTIPOLIS ? MÉDITERRANÉE 2004 route des Lucioles -BP 93, p.8396, 1997. ,
DOI : 10.1006/nimg.1996.0248