. Actel, Actel : Low Power and Mixed Signal FPGA solutions

J. P. Asselin-de-beauville and F. Z. Kettaf, Bases théorique pour l'apprentissage et la décision en reconnaissance des formes. Cépadues, 2005.

J. Batlle, P. Marti, J. Ridao, and . Amat, A New FPGA/DSP-Based Parallel Architecture for Real-Time Image Processing, Real-Time Imaging, vol.8, issue.5, pp.345-356, 2002.
DOI : 10.1006/rtim.2001.0273

A. Bouridane, D. Crookes, P. Donachy, L. Alotaibi, and K. Benkrid, A high level FPGA-based abstract machine for image processing, Journal of Systems Architecture, vol.45, issue.10, pp.809-824, 1999.
DOI : 10.1016/S1383-7621(98)00040-X

F. Cheng and Y. Ying, Machine vision inspection of rice seed based on Hough transform, Journal of Zhejiang University SCIENCE, vol.5, issue.6, pp.663-667, 2004.
DOI : 10.1631/jzus.2004.0663

F. Cheng, Z. Liu, and Y. Ying, Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed, Rice Science, vol.12, issue.1, pp.13-18, 2005.

D. Chessel and M. Hanafi, Analyses de la co-inertie de K nuages de points, Revue de statistique appliquée, pp.35-60, 1996.

S. Chevallier, D. Bertrand, A. Kohler, and P. Courcoux, Application of PLS-DA in multivariate image analysis, Journal of Chemometrics, vol.49, issue.5, pp.221-229, 2006.
DOI : 10.1002/cem.994

Y. Chtioui, Reconnaissance automatique des semences par vision artificielle basée sur des approches statistiques et connexionnistes, 1997.

Y. Chtioui, D. Barba, and D. Bertrand, Reduction of the size of the learning data in a probabilistic neural network by hierarchical clustering. Application to the discrimination of seeds by artificial vision, Chemometrics and Intelligent Laboratory Systems, vol.35, issue.2, pp.175-186, 1996.
DOI : 10.1016/S0169-7439(96)00065-2

Y. Chtioui, D. Bertrand, and D. Barba, Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision, Journal of the Science of Food and Agriculture, vol.76, issue.1, pp.77-86, 1998.
DOI : 10.1002/(SICI)1097-0010(199801)76:1<77::AID-JSFA948>3.0.CO;2-9

Y. Chtioui, D. Bertrand, and Y. Dattée, Identification of Seeds by Colour Imaging: Comparison of Discriminant Analysis and Artificial Neural Network, Journal of the Science of Food and Agriculture, vol.71, issue.4, pp.433-441, 1996.
DOI : 10.1002/(SICI)1097-0010(199608)71:4<433::AID-JSFA596>3.0.CO;2-B

Y. Chtioui, D. Bertrand, D. Barba, and M. F. Devaux, Comparison of multilayer perceptron and probabilistic neural networks in artificial vision. Application to the discrimination of seeds, Journal of Chemometrics, vol.11, issue.2, pp.111-129, 1997.
DOI : 10.1002/(SICI)1099-128X(199703)11:2<111::AID-CEM455>3.0.CO;2-V

Y. Chtioui, D. Bertrand, D. Barba, and Y. Dattée, Application of fuzzy C-Means clustering for seed discrimination by artificial vision Chemometrics and intelligent laboratory systems 38, pp.75-87, 1997.

P. Egelberg, O. Mansson, and C. Petersen, <title>Assessing cereal grain quality with a fully automated instrument using artificial neural network processing of digitized color video images</title>, Optics in Agriculture, Forestry, and Biological Processing, pp.2345-146, 1994.
DOI : 10.1117/12.198900

B. Escofier and J. Pages, Mise en oeuvre de l'analyse factorielle multiple pour les tableaux numériques qualitatifs ou mixtes RR-429, 1985.

X. Feng, T. Pearson, F. Dowell, and N. Zhang, Detecting Vitreous Wheat Kernels Using Reflectance and Transmittance Image Analysis, Cereal Chemistry, vol.81, issue.5, pp.594-597, 2004.

R. E. Fisher, P. R. Biljana-tadic-galeb, and . Yoder, Optical system design, 2008.

M. M. Galloway, Texture analysis using gray level run lengths, Computer Graphics and Image Processing, vol.4, issue.2, pp.172-179, 1975.
DOI : 10.1016/S0146-664X(75)80008-6

J. Gaudin, Colorimétrie appliquée à la vidéo. Dunod, 2006.

P. M. Granitto, P. F. Verdes, and H. A. Ceccatto, Large-scale investigation of weed seed identification by machine vision, Computers and Electronics in Agriculture, vol.47, issue.1, pp.15-24, 2005.
DOI : 10.1016/j.compag.2004.10.003

. Mersereau, Demosaicking: Color filter array interpolation in single-chip digital cameras, IEEE Signal Processing Magazine, vol.22, issue.1, pp.44-54, 2005.

G. Hamid, Quality Assessment Of Product In Bulk Flow. Ed. World Intellectual Property Organization, Patent WO, 2004.

R. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol.67, issue.5, 1979.
DOI : 10.1109/PROC.1979.11328

A. Jain and D. Zongker, Feature selection: evaluation, application, and small sample performance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.2, pp.153-158, 1997.
DOI : 10.1109/34.574797

D. S. Jayas, C. E. Murray, and N. R. Bulley, An automated seed presentation device for use in machine vision identification of grain, Canadian agricultural engineering, vol.41, issue.2, pp.113-118, 1999.

P. Keefe, Observation concerning shape variation in wheat grains, Seed Science and Technologie, vol.18, pp.629-640, 1990.

P. D. Keefe and S. R. Draper, The measurement of new characters for cultivar identification in wheat using machine vision, pp.715-724, 1986.

N. Kiryati and D. Maydan, Calculating geometric properties from fourier representation, Pattern Recognition, vol.22, issue.5, pp.469-475, 1989.
DOI : 10.1016/0031-3203(89)90017-4

L. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, IEEE Transactions on Neural Networks, vol.18, issue.3, 2004.
DOI : 10.1109/TNN.2007.897478

M. Kunt, Reconnaissance des formes et analyse de scènes, Presses polytechniques et universitaires romandes, 2000.

F. S. Lai, I. Zayas, and Y. Pomeranz, Application of pattern recognition techniques in the analysis of cereal grains, Cereal chemistry, vol.63, issue.2, pp.168-172, 1986.

C. Lavit, Analyse conjointe de tableaux quantitatifs, 1988.

Z. Y. Liu, F. Cheng, Y. B. Ying, and X. Q. Rao, Identification of rice seed varieties using neural network, Journal of Zhejiang University SCIENCE, vol.6, issue.11, pp.1095-1100, 2005.
DOI : 10.1631/jzus.2005.B1095

F. Lohier, Méthodologies de programmation et évaluation des processeurs de traitement de signal parallèles pour le traitement d'images en temps réel, 2000.

S. Majumdar and D. Jayas, CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: IV. COMBINED MORPHOLOGY, COLOR, AND TEXTURE MODELS, Transactions of the ASAE, vol.43, issue.6, pp.1689-1694, 2000.
DOI : 10.13031/2013.3069

S. Majumdar and D. S. Jayas, CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: I. MORPHOLOGY MODELS, Transactions of the ASAE, vol.43, issue.6, pp.1669-1675, 2000.
DOI : 10.13031/2013.3107

S. Majumdar and D. S. Jayas, CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: II. COLOR MODELS, Transactions of the ASAE, vol.43, issue.6, pp.1677-1680, 2000.
DOI : 10.13031/2013.3067

S. Majumdar and D. S. Jayas, CLASSIFICATION OF CEREAL GRAINS USING MACHINE VISION: III. TEXTURE MODELS, Transactions of the ASAE, vol.43, issue.6, pp.1681-1687, 2000.
DOI : 10.13031/2013.3068

M. B. Mcdonald, A. F. Evans, and M. A. Bennet, Using scanners to improve seed and seedling evaluations, Science and Technologie, pp.683-689, 2001.

V. Muracciole, P. Plainchault, D. Bertrand, and M. Mannino, Development of an automated device for sorting seeds -application on sunflower seeds, ICINCO, pp.311-318, 2007.

M. Nair, Dockage identification in wheat using machine vision . Thesis-Master of Science, The University of Manitoba, 1997.

B. Ni, M. R. Paulsen, and J. F. Reid, Size grading of corn kernels with machine vision, ASAE, vol.14, issue.5, pp.567-571, 1998.

M. Nixon and A. Aguado, Feature Extraction and Image Processing second edition Associated press, 2008.

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.62-66, 1979.
DOI : 10.1109/TSMC.1979.4310076

J. Paliwal, N. S. Visen, D. S. Jayas, and N. D. White, Cereal Grain and Dockage Identification using Machine Vision, Biosystems Engineering, vol.85, issue.1, pp.51-57, 2003.
DOI : 10.1016/S1537-5110(03)00034-5

J. Paliwal, N. S. Visen, D. S. Jayas, and N. D. White, Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification, Biosystems Engineering, vol.85, issue.4, pp.405-413, 2003.
DOI : 10.1016/S1537-5110(03)00083-7

T. Pearson, Machine Vision System for Automated Detection of Stained Pistachio Nuts, Proceedings Of Optics in Agriculture, Forestery, and Biological Processing 2345, pp.95-103, 1994.
DOI : 10.1006/fstl.1996.0030

P. E. Petersen and G. W. Krutz, Automatic identification of weed seeds by colour machine vision, pp.193-208, 1992.

P. Plainchault, S. Tourveille, D. Demilly, D. Bertrand, and A. Feutry, Development of an imaging system for the high-speed identification of seeds, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498), pp.499-501, 2003.
DOI : 10.1109/ICSENS.2003.1278988

C. Poynton, A technical introduction to digital video, 2003.

B. Rasmus, PARAFAC. Tutorial and applications, pp.149-171, 1997.

N. Sakai, S. Yonekawa, and A. Matsuzaki, Two-dimensional image analysis of the shape of rice and its application to separating varieties, Journal of Food Engineering, vol.27, issue.4, pp.397-407, 1996.
DOI : 10.1016/0260-8774(95)00022-4

G. Saporta, Probabilités et statistique : analyse des données 2ème édition, 2006.

W. Stallings, Computer organization and architecture: design for performance, 2006.

S. J. Symons and R. G. Fulcher, Determination of wheat kernel morphological variation by digital image analysis: I. Variation in Eastern Canadian milling quality wheats, Journal of Cereal Science, vol.8, issue.3, pp.211-218, 1988.
DOI : 10.1016/S0733-5210(88)80032-8

M. A. Tahir, A. Bouridiane, and F. Kurugollu, An FPGA Based Coprocessor for GLCM and Haralick Texture Features and their Application in Prostate Cancer Classification, Analog Integrated Circuits and Signal Processing, vol.24, issue.11, pp.205-215, 2005.
DOI : 10.1007/s10470-005-6793-2

M. Tenenhaus, La régression PLS: Théorie et pratique, 1998.

A. J. Travis and S. R. Draper, A computer based system for the recognition of seed shape, Seed Science and Technologie, vol.13, pp.813-820, 1985.

S. Tuffery, Data mining et statistique décisionnelle, 2007.

N. S. Visen, J. Paliwala, D. S. Jayas, and N. D. White, AE???Automation and Emerging Technologies, Specialist Neural Networks for Cereal Grain Classification, pp.151-159, 2002.
DOI : 10.1006/bioe.2002.0064

Y. Wan, Kernel handling performance of an automatic grain quality inspection, Transaction ASAE, vol.45, issue.2, pp.369-378, 2002.

W. Wang and J. Paliwal, Separation and identification of touching kernels and dockage components in digital images, Canadian biosystems engineering, vol.48, pp.7-8, 2006.

T. H. Wonnacott, Statistique : Economie -Gestion -Sciences -Médecine (avec exercices d'application), Economica, 1998.

. Xilinx, FPGA and CPLD solutions from Xilinx

?. Multi, Port Memory Controller Reference Design

C. Yang, S. O. Prasher, and J. A. Landry, Weed recognition in corn fields using backpropagation neural network models, Canadian Biosystems Engineering, vol.4422, pp.7-15, 2002.

I. Zayas, Y. Pomeranz, and F. S. Lai, Discrimination between Arthur and Arkan wheats by image analysis, Cereal chemistry, vol.62, issue.6, pp.478-480, 1985.

I. Zayas, Y. Pomeranz, and F. S. Lai, Discrimination of wheat and nonwheat components in grain samples by image analysis, Cereal Chemistry, vol.66, issue.3, pp.233-237, 1989.

I. Y. Zayas, D. B. Bechtel, J. D. Wilson, and R. E. Dempster, Distinguishing selected hard and soft red winter wheats by image analysis of starch granules, Cereal chemistry, pp.71-82, 1994.