L. Vega, G. Benezeth, Y. Marzani, F. Boochs, and F. , Modular method of detection, localization, and counting of multiple-taxon pollen apertures using bag-of-words, Journal of Electronic Imaging, vol.23, issue.5, p.53025, 2014.
DOI : 10.1117/1.JEI.23.5.053025

L. Vega, G. Benezeth, Y. Marzani, F. Boochs, and F. , Analysis of relevant features for pollen classification, Artificial Intelligence Applications and Innovations IFIP Advances in Information and Communication Technology, pp.395-404, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095828

L. Vega, G. Benezeth, Y. Marzani, F. Boochs, and F. , Classification of Pollen Apertures Using Bag of Words, Image Analysis and Processing, pp.712-721, 2013.
DOI : 10.1007/978-3-642-41181-6_72
URL : https://hal.archives-ouvertes.fr/hal-00857927

L. Vega, G. Benezeth, Y. Uhler, M. Boochs, F. Marzani et al., Sketch of an automatic image based pollen detection system, pp.202-209, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00824014

S. J. Ahn, W. Rauh, and H. Warnecke, Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola, Pattern Recognition, vol.34, issue.12, pp.342283-2303, 2001.
DOI : 10.1016/S0031-3203(00)00152-7

G. Allen, An automated pollen recognition system, 2006.

G. Allen, B. Hodgson, S. Marsland, G. Arnold, R. Flemmer et al., Automatic recognition of light microscope pollen images, Image Vision and Computing New Zealand, pp.355-360, 2006.

K. Bergmann, Aufkommen der allergologisch relevanten Pollen in Deutschland von, 2001.

G. Borgefors, Distance transformations in digital images Computer vision, graphics , and image processing, pp.344-371, 1986.

A. Boucher, P. J. Hidalgo, M. Thonnat, J. Belmonte, C. Galan et al., Development of a semi-automatic system for pollen recognition, Aerobiologia, vol.18, pp.3-4195, 2002.

J. Bousquet, Allergic Rhinitis and its Impact on Asthma (ARIA), Clinical <html_ent glyph="@amp;" ascii="&"/> Experimental Allergy Reviews, vol.8, issue.1, pp.8-160, 2008.
DOI : 10.1034/j.1398-9995.2000.00526.x
URL : https://hal.archives-ouvertes.fr/inserm-00815533

R. Buchner and M. Weber, onwards). paldat -a palynological database: Descriptions, illustrations, identification, and information retrieval, 2000.

H. Byun and S. Lee, Applications of support vector machines for pattern recognition: A survey, Pattern recognition with support vector machines, pp.213-236, 2002.

Y. Cao, C. Wang, Z. Li, L. Zhang, and L. Zhang, Spatial-bag-of-features, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3352-3359, 2010.
DOI : 10.1109/CVPR.2010.5540021

S. Chabrier, B. Stoll, and J. And-goujon, SVM texture classification for tropical vegetation mapping, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, pp.85270-85270, 2012.
DOI : 10.1117/12.977182

C. Chen, E. A. Hendriks, R. P. Duin, J. H. Reiber, P. S. Hiemstra et al., Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort, Aerobiologia, vol.80, issue.Suppl 1, pp.275-284, 2006.
DOI : 10.1007/s10453-006-9040-0

C. Costa, P. Menesatti, G. Paglia, F. Pallottino, J. Aguzzi et al., Quantitative evaluation of Tarocco sweet orange fruit shape using optoelectronic elliptic Fourier based analysis, Postharvest biology and Technology, pp.38-47, 2009.
DOI : 10.1016/j.postharvbio.2009.05.001

J. S. Crampton, Elliptic Fourier shape analysis of fossil bivalves: some practical considerations, Lethaia, vol.66, issue.2, pp.179-186, 1995.
DOI : 10.2307/2413430

G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, Visual categorization with bags of keypoints, Workshop on statistical learning in computer vision, European Conference on Computer Vision, pp.1-22, 2004.

P. F. Culverhouse, N. Macleod, R. Williams, M. C. Benfield, R. M. Lopes et al., An empirical assessment of the consistency of taxonomic identifications, Marine Biology Research, vol.32, issue.1, pp.73-84, 2014.
DOI : 10.1016/S0262-8856(98)00159-0

L. Da-fontoura-da-costa, C. Jr, and R. M. , Shape analysis and classification: theory and practice, 2000.
DOI : 10.1201/9781420037555

D. Amato, G. Cecchi, L. Bonini, S. Nunes, C. Annesi-maesano et al., Allergenic pollen and pollen allergy in europe, Allergy, issue.9, pp.62976-990, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00596405

R. Dell-'anna, P. Lazzeri, M. Frisanco, F. Monti, F. M. Campeggi et al., Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning, Analytical and bioanalytical chemistry, vol.394, issue.5, pp.1443-1452, 2009.

R. O. Duda and P. E. Hart, Use of the Hough transformation to detect lines and curves in pictures, Communications of the ACM, vol.15, issue.1, pp.11-15, 1972.
DOI : 10.1145/361237.361242

M. S. Dykewicz and D. L. Hamilos, Rhinitis and sinusitis, Journal of Allergy and Clinical Immunology, vol.125, issue.2, pp.103-115, 2010.
DOI : 10.1016/j.jaci.2009.12.989

R. Egerton, The Scanning Electron Microscope, Physical Principles of Electron Microscopy, pp.125-153, 2005.
DOI : 10.1007/978-3-319-39877-8_5

G. Erdtman, An Introduction to Pollen Analysis, Soil Science, vol.57, issue.3, 1943.
DOI : 10.1097/00010694-194403000-00006

T. Fawcett, Roc graphs: Notes and practical considerations for researchers, pp.2003-2007, 2003.

I. France, A. Duller, H. Lamb, and G. Duller, A comparative study of approaches to automatic pollen identification, Proceedings of the British Machine Vision Confer- ence, 1997.

S. Goto, H. Iwata, S. Shibano, K. Ohya, A. Suzuki et al., Fruit shape variation in Fraxinus mandshurica var. japonica characterized using elliptic Fourier descriptors and the effect on flight duration, Ecological Research, vol.94, issue.108, pp.733-738, 2005.
DOI : 10.1007/s11284-005-0090-5

A. Goyal and E. Walia, Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval, Signal, Image and Video Processing, pp.1273-1289, 2014.
DOI : 10.1007/s11760-012-0353-x

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, pp.1-3389, 2002.

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions, issue.36, pp.610-621, 1973.

K. Holt, G. Allen, R. Hodgson, S. Marsland, and J. Flenley, Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory, Review of Palaeobotany and Palynology, vol.167, issue.3-4, pp.3-4175, 2011.
DOI : 10.1016/j.revpalbo.2011.08.006

D. Huang, C. Shan, M. Ardabilian, Y. Wang, C. et al., Local Binary Patterns and Its Application to Facial Image Analysis: A Survey, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.41, issue.6, pp.41765-781, 2011.
DOI : 10.1109/TSMCC.2011.2118750
URL : https://hal.archives-ouvertes.fr/hal-01354386

R. Huorong, Y. Xinxin, Z. Yan, C. Rui, S. Jianwei et al., Relative gradient local binary patterns method for face recognition under varying illuminations, Journal of Electronic Imaging, vol.22, issue.4, pp.43013-043013, 2013.
DOI : 10.1117/1.JEI.22.4.043013

H. Iwata, S. Niikura, S. Matsuura, Y. Takano, and Y. Ukai, Evaluation of variation of root shape of japanese radish (Raphanus sativus L.) based on image analysis using elliptic Fourier descriptors, Euphytica, vol.102, issue.2, pp.143-149, 1998.
DOI : 10.1023/A:1018392531226

S. T. Jackson, R. S. Webb, K. H. Anderson, J. T. Overpeck, T. W. Iii et al., Vegetation and environment in Eastern North America during the Last Glacial Maximum, Quaternary Science Reviews, vol.19, issue.6, pp.489-508, 2000.
DOI : 10.1016/S0277-3791(99)00093-1

T. Joachims, Text categorization with Support Vector Machines: Learning with many relevant features, European Conference on Machine Learning, pp.137-142, 1998.
DOI : 10.1007/BFb0026683

S. Kawashima, B. Clot, T. Fujita, Y. Takahashi, and K. Nakamura, An algorithm and a device for counting airborne pollen automatically using laser optics, Atmospheric Environment, vol.41, issue.36, pp.417987-7993, 2007.
DOI : 10.1016/j.atmosenv.2007.09.019

C. Kimme, D. Ballard, and J. Sklansky, Finding circles by an array of accumulators, Communications of the ACM, vol.18, issue.2, pp.120-122, 1975.
DOI : 10.1145/360666.360677

J. J. Koenderink and A. J. Van-doorn, Representation of local geometry in the visual system, Biological Cybernetics, vol.53, issue.6, pp.367-375, 1987.
DOI : 10.1007/BF00318371

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

S. H. Landsmeer, E. A. Hendriks, L. A. De-weger, J. H. Reiber, and B. C. Stoel, Detection of pollen grains in multifocal optical microscopy images of air samples, Microscopy research and technique, pp.72424-430, 2009.
DOI : 10.1002/jemt.20688

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68
URL : https://hal.archives-ouvertes.fr/inria-00548585

P. Li, W. Treloar, J. Flenley, and L. Empson, Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains, Journal of Quaternary Science, vol.30, issue.8, pp.19755-762, 2004.
DOI : 10.1002/jqs.874

J. W. Lichtman and J. Conchello, Fluorescence microscopy, Nature Methods, vol.176, issue.12, pp.910-919, 2005.
DOI : 10.1038/nmeth817

X. Lladóllad´lladó, A. Oliver, J. Freixenet, R. Martí, and J. Martí, A textural approach for mass false positive reduction in mammography, Computerized Medical Imaging and Graphics, issue.6, pp.33415-422, 2009.

L. ´-opez-sastre, R. J. Tuytelaars, T. Acevedo-rodríguez, F. J. Maldonado-bascónbasc´bascón, and S. , Towards a more discriminative and semantic visual vocabulary, Computer Vision and Image Understanding, vol.115, issue.3, pp.415-425, 2011.
DOI : 10.1016/j.cviu.2010.10.009

P. Mandrioli, Method for sampling and counting of airborne pollen and fungal spores, Institute of Atmospheric and Oceanic Sciences (ISAO), National Research Council (CNR), 2000.

H. K. Mebatsion, J. Paliwal, J. , and D. S. , A novel, invariant elliptic Fourier coefficient based classification of cereal grains, Biosystems Engineering, vol.111, issue.4, pp.422-428, 2012.
DOI : 10.1016/j.biosystemseng.2012.01.009

P. Menesatti, C. Costa, G. Paglia, F. Pallottino, S. D-'andrea et al., Shape-based methodology for multivariate discrimination among Italian hazelnut cultivars, Biosystems Engineering, vol.101, issue.4, pp.417-424, 2008.
DOI : 10.1016/j.biosystemseng.2008.09.013

P. Mhangara and J. Odindi, Potential of texture-based classification in urban landscapes using multispectral aerial photos, South African Journal of Science, vol.109, issue.3/4, pp.3-41, 2013.
DOI : 10.1590/sajs.2013/1273

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.27, issue.10, pp.1615-1630, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548227

D. Mildenhall, P. Wiltshire, and V. Bryant, Forensic palynology: Why do it and how it works, Forensic Science International, vol.163, issue.3, pp.163-172, 2006.
DOI : 10.1016/j.forsciint.2006.07.012

K. Mitsumoto, K. Yabusaki, and H. Aoyagi, Classification of pollen species using autofluorescence image analysis, Journal of Bioscience and Bioengineering, vol.107, issue.1, pp.90-94, 2009.
DOI : 10.1016/j.jbiosc.2008.10.001

J. Mullins and J. Emberlin, Sampling pollens, Journal of Aerosol Science, vol.28, issue.3, pp.365-370, 1997.
DOI : 10.1016/S0021-8502(96)00439-9

L. Nanni, A. Lumini, and S. Brahnam, Survey on LBP based texture descriptors for image classification, Expert Systems with Applications, vol.39, issue.3, pp.3634-3641, 2012.
DOI : 10.1016/j.eswa.2011.09.054

A. Negrini, Pollens as allergens, Aerobiologia, vol.46, issue.II part, pp.9-15, 1992.
DOI : 10.1007/BF02291321

J. C. Neto, G. E. Meyer, D. D. Jones, and A. K. Samal, Plant species identification using elliptic Fourier leaf shape analysis. Computers and electronics in agriculture, pp.121-134, 2006.

M. Nixon, Feature extraction & image processing, 2008.

E. Nowak, F. Jurie, and B. Triggs, Sampling Strategies for Bag-of-Features Image Classification, European Conference on Computer Vision, pp.490-503, 2006.
DOI : 10.1007/11744085_38
URL : https://hal.archives-ouvertes.fr/hal-00203752

O. Higgins and P. , Methodological issues in the description of forms. Fourier descriptors and their applications in biology, pp.74-105, 1997.

T. Ojala, M. Pietikäinenpietik¨pietikäinen, and D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, vol.29, issue.1, pp.51-59, 1996.
DOI : 10.1016/0031-3203(95)00067-4

T. Ojala, M. Pietikäinenpietik¨pietikäinen, and T. , Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, European Conference on Computer Vision, pp.404-420, 2000.
DOI : 10.1109/TPAMI.2002.1017623

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.285-29623, 1975.
DOI : 10.1109/TSMC.1979.4310076

D. Pelleg and A. W. Moore, X-means: Extending k-means with efficient estimation of the number of clusters, International Conference on Machine Learning, pp.727-734, 2000.

A. Perveen, A contribution to the pollen morphology of family Gramineae, World Applied Sciences Journal, vol.1, issue.2, pp.60-65, 2006.

F. Provost and T. Fawcett, Robust classification for imprecise environments, Machine Learning, pp.203-231, 2001.

W. Punt, P. Hoen, S. Blackmore, L. Thomas, and A. , Glossary of pollen and spore terminology, Review of Palaeobotany and Palynology, vol.143, issue.1-2, pp.1-81, 2007.
DOI : 10.1016/j.revpalbo.2006.06.008

M. Ranzato, P. Taylor, J. House, R. Flagan, Y. Lecun et al., Automatic recognition of biological particles in microscopic images, Pattern Recognition Letters, vol.28, issue.1, pp.31-39, 2007.
DOI : 10.1016/j.patrec.2006.06.010

. National-de-surveillance-aerobiologique, Les pollens: Principaux pollens allergisants, 2014.

Y. Rodriguez, Face detection and verification using local binary patterns, 2006.

M. Rodríguez-damí-an, E. Cernadas, A. Formella, and A. Gonzálezgonz´gonzález, Automatic identification and classification of pollen of the Urticaceae family, Advanced Concepts for Intelligent Vision Systems, Proceedings of, pp.38-45, 2003.

F. J. Rohlf and J. W. Archie, A Comparison of Fourier Methods for the Description of Wing Shape in Mosquitoes (Diptera: Culicidae), Systematic Zoology, vol.33, issue.3, pp.302-317, 1984.
DOI : 10.2307/2413076

O. Ronneberger, 3D invariants for automated pollen recognition, 2007.

O. Ronneberger, Q. Wang, and H. Burkhardt, 3D Invariants with High Robustness to Local Deformations for Automated Pollen Recognition, Pattern Recognition, pp.425-435, 2007.
DOI : 10.1007/978-3-540-74936-3_43

T. Saito and J. Toriwaki, New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications, Pattern Recognition, vol.27, issue.11, pp.1551-1565, 1994.
DOI : 10.1016/0031-3203(94)90133-3

S. Scharring, A. Brandenburg, G. Breitfuss, H. Burkhardt, W. Dunkhorst et al., Online Monitoring of Airborne Allergenic Particles (OMNIBUSS), Biophotonics, pp.31-87, 2006.
DOI : 10.1002/3527608842.ch2

D. Semwogerere and E. R. Weeks, Confocal Microscopy, Encyclopedia of Biomaterials and Biomedical Engineering, pp.1-10, 2005.
DOI : 10.1201/b18990-68

S. Theodoridis and K. K. , Pattern Recognition, Pattern Recognition, 2006.
DOI : 10.1016/B0-12-227240-4/00132-5

M. Sivaguru, L. Mander, G. Fried, and S. W. Punyasena, Capturing the Surface Texture and Shape of Pollen: A Comparison of Microscopy Techniques, PLoS ONE, vol.18, issue.6, p.39129, 2012.
DOI : 10.1371/journal.pone.0039129.s019

C. G. Soldevilla, P. C. Gonzálezgonz´gonzález, P. A. Teno, and E. D. Vilches, Spanish Aerobiology Network (REA): management and quality manual, 2007.

S. Suzuki and K. Abe, Topological structural analysis of digitized binary images by border following, Computer Vision, Graphics, and Image Processing, vol.30, issue.1, pp.32-46, 1985.
DOI : 10.1016/0734-189X(85)90016-7

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.
DOI : 10.1111/1467-9868.00293

J. Y. Tou, Y. H. Tay, and P. Y. Lau, A Comparative Study for Texture Classification Techniques on Wood Species Recognition Problem, 2009 Fifth International Conference on Natural Computation, pp.8-12, 2009.
DOI : 10.1109/ICNC.2009.594

Q. H. Truong, Knowledge-based 3D point clouds processing, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00977434

D. Unay and A. Ekin, Intensity versus texture for medical image search and retrival, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.241-244, 2008.
DOI : 10.1109/ISBI.2008.4540977

B. Wang, Y. Liu, W. Xiao, W. Xu, and M. Zhang, Position and locality constrained soft coding for human action recognition, Journal of Electronic Imaging, vol.22, issue.4, pp.41118-041118, 2013.
DOI : 10.1117/1.JEI.22.4.041118

R. Wayne, Light and video microscopy, 2009.

K. Williams, J. Munkvold, and M. Sorrells, Comparison of digital image analysis using elliptic Fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.), Euphytica, vol.62, issue.1, pp.99-116, 2013.
DOI : 10.1007/s10681-012-0783-0

Y. Yoshioka, H. Iwata, R. Ohsawa, and S. Ninomiya, Analysis of Petal Shape Variation of Primula sieboldii by Elliptic Fourier Descriptors and Principal Component Analysis, Annals of Botany, vol.94, issue.5, pp.657-664, 2004.
DOI : 10.1093/aob/mch190

E. Zhang and M. Mayo, Enhanced spatial pyramid matching using log-polarbased image subdivision and representation, Digital Image Computing: Techniques and Applications, International Conference on, pp.208-213, 2010.

Y. Zhang, D. Fountain, R. Hodgson, J. Flenley, and S. Gunetileke, Towards automation of palynology 3: pollen pattern recognition using Gabor transforms and digital moments, Journal of Quaternary Science, vol.19, issue.8, pp.19763-768, 2004.
DOI : 10.1002/jqs.875

N. Zulpe and V. Pawar, GLCM textural features for brain tumor classification, International Journal of Computer Science Issues, vol.9, issue.3, 2012.