Search

Your search keyword '"Ludovic Journaux"' showing total 69 results
Start Over Author "Ludovic Journaux"
69 results on '"Ludovic Journaux"'

1. 3D Image Acquisition System Based on Shape from Focus Technique

Author

Pierre Gouton, Jean-Claude Simon, Ludovic Journaux, Frédéric Cointault, and Bastien Billiot

Subjects
3D image acquisition system, shape from focus, focus measure, agronomic scenes, Chemical technology, TP1-1185
Abstract

This paper describes the design of a 3D image acquisition system dedicated to natural complex scenes composed of randomly distributed objects with spatial discontinuities. In agronomic sciences, the 3D acquisition of natural scene is difficult due to the complex nature of the scenes. Our system is based on the Shape from Focus technique initially used in the microscopic domain. We propose to adapt this technique to the macroscopic domain and we detail the system as well as the image processing used to perform such technique. The Shape from Focus technique is a monocular and passive 3D acquisition method that resolves the occlusion problem affecting the multi-cameras systems. Indeed, this problem occurs frequently in natural complex scenes like agronomic scenes. The depth information is obtained by acting on optical parameters and mainly the depth of field. A focus measure is applied on a 2D image stack previously acquired by the system. When this focus measure is performed, we can create the depth map of the scene.

Published
2013
Full Text
View/download PDF

24. Proximities in dimensionality reduction

Author

John Aldo Lee, Cyril de Bodt, Ludovic Journaux, Lucile Sautot, and UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique

Subjects
Neighbor embedding, Dimensionality reduction, Similarity, Embedding
Abstract

Dimensionality reduction aims at representing high-dimensional data in a lower-dimensional representation, while preserving their structure (clusters, outliers, manifold). Dimensionality reduction can be used for exploratory data visualization, data compression, or as a preprocessing to some other analysis in order to alleviate the curse of dimensionality. Data structure is usually quantified with indicators, like covariance between variables, or pairwise proximity relationships, like scalar products, distances, similarities, or neighbourhoods. One objective of this chapter is to provide an overview of some classical and more recent methods of dimensionality reduction, to shed some light on them from the perspective of analyzing proximities, and to illustrate them with multivariate data that could be typically encountered in social sciences. Complementary aspects like quality assessment and alternative metrics are briefly developed.

Published
2022
Full Text
View/download PDF

26. Qui a peur du changement climatique?

Author

Lucile Sautot, Eric Chraibi, Jacques Fize, Sébastien Peillet, Ludovic Journaux, Flavie Cernesson, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Bourgogne [Dijon] (LIB), Université de Bourgogne (UB), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Sautot, Lucile

Subjects
Changement climatique, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [SHS.GEO] Humanities and Social Sciences/Geography, Global warming, Grand Débat National, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, [SHS.GEO]Humanities and Social Sciences/Geography, rand Débat National, ComputingMilieux_MISCELLANEOUS
Abstract

The French "Grand Débat National" (Great National Debate) was animportant political event in 2019. Using the online proposals collected during thisconsultation, we propose a representation of the feeling of the impact of climate changeamong the contributors to the "Grand Débat National" in Metropolitan France. Weanalyze the causes of this feeling, through a supervised analysis of the contributions(via the Great Annotation) and we show the complementary interest of an unsuper-vised analysis (by extracting keywords). We show the richness of the data set that constitutes the "Grand Débat National", and the analytical stakes around these data.We also point out some important limitations to be taken into account, including therepresentativeness of the dataset., RÉSUMÉ.Le Grand Débat National a été un événement politique important de l’année2019. En utilisant les propositions en ligne récoltées lors de cette consultation, nousproposons une représentation du sentiment d’impact du changement climatique parmiles contributeurs du Grand Débat National en France Métropolitaine. Nous analysonsles causes de ce sentiment, grâce à une analyse supervisée des contributions (via laGrande Annotation) et nous montrons l’intérêt complémentaire d’une analyse non-supervisée (par extraction de mots clés). Nous montrons la richesse du jeu de donnéesque constitue le Grand Débat National, et les enjeux analytiques autour de ces don-nées. Nous soulevons également quelques limites importantes à prendre en comptedont la représentativité du jeu de données

Published
2019

27. Effect of high hydrostatic pressure on extraction of B-phycoerythrin from Porphyridium cruentum: Use of confocal microscopy and image processing

Author

Ludovic Journaux, Nathalie Cayot, Rémi Pradelles, Emmanuel Denimal, Jean-Marie Perrier-Cornet, Pascale Winckler, Thierry Tran, Camille Loupiac, Céline Lafarge, John Aldo Lee, Procédés Alimentaires et Microbiologiques [Dijon] (PAM), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Service d’Appui à la Recherche - AgroSup Dijon, AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire d'Informatique de Bourgogne [Dijon] (LIB), Université de Bourgogne (UB), Université Catholique de Louvain = Catholic University of Louvain (UCL), Dispositif Inter-régional d'Imagerie Cellulaire [Dijon] (DImaCell), Procédés Alimentaires et Microbiologiques (PAM), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ingénierie et biologie cellulaire et tissulaire (IBCT (ex IFR133)), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Microphyt, Regional Council of Bourgogne - Franche Comte, and 'Fonds Europeens de Developpement Regional' (FEDER) Funding Text : This work was supported by the Regional Council of Bourgogne - Franche Comte and the 'Fonds Europeens de Developpement Regional' (FEDER) for the 'PARI Alim+' project

Subjects
020209 energy, Hydrostatic pressure, Porphyridium cruentum, Extraction, 02 engineering and technology, law.invention, 0404 agricultural biotechnology, High hydrostatic pressure, Image processing, Confocal microscopy, law, Spectrophotometry, 0202 electrical engineering, electronic engineering, information engineering, medicine, Denaturation (biochemistry), Confocal laser scanning microscopy, B-phycoerythrin, medicine.diagnostic_test, biology, Chemistry, Extraction (chemistry), 04 agricultural and veterinary sciences, biology.organism_classification, 040401 food science, Fluorescence, biology.protein, Biophysics, Agronomy and Crop Science, Phycoerythrin, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The aim of the study was to extract B-phycoerythrin from Porphyridium cruentum while preserving its structure. The high hydrostatic pressure treatments were chosen as extraction technology. Different methods have been used to observe the effects of the treatment: spectrophotometry and confocal laser scanning microscopy followed by image processing analysis. Image processing led to the generation of masks used for the identification of three clusters: intra, extra and intercellular. All methods showed that high hydrostatic pressure treatments between 50 and 500 MPa failed to extract B-phycoerythrin from Porphyridium cruentum cells. The fluorescence emission was negatively impacted by high hydrostatic pressure treatment from 400 MPa for the extracellular and intercellular cluster and from 500 MPa for the intercellular cluster. These results suggest that high pressure treatments could induce the denaturation of B-phycoerythrin in all clusters but with different intensities depending on the cluster.

Published
2019
Full Text
View/download PDF

28. Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates in Saccharomyces cerevisiae Using a Textural Approach

Author

Stéphane Guyot, Lucie Bertheau, Emmanuel Denimal, Ambroise Marin, Ludovic Journaux, Paul Molin, Service d’Appui à la Recherche - AgroSup Dijon, AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Procédés Alimentaires et Microbiologiques [Dijon] (PAM), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Procédés Microbiologiques et Biotechnologiques (PMB), Procédés Alimentaires et Microbiologiques (PAM), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Procédés Alimentaires et Microbiologiques [Dijon] (PAM), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne (UB), and Laboratoire d'Informatique de Bourgogne [Dijon] (LIB)

Subjects
Cytoplasm, Saccharomyces cerevisiae Proteins, Zernike polynomials, Computer science, Saccharomyces cerevisiae, Green Fluorescent Proteins, 0211 other engineering and technologies, sub-cellular structures, Context (language use), 02 engineering and technology, Protein aggregation, ribonucleo-protein aggregates, Cytoplasmic Granules, Models, Biological, Poly(A)-Binding Proteins, 03 medical and health sciences, symbols.namesake, Protein Aggregates, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Generalized Fourier descriptors, Instrumentation, 030304 developmental biology, 021110 strategic, defence & security studies, 0303 health sciences, Fusion, Haralick, biology, Zernike, A protein, biology.organism_classification, Fourier transform, Microscopy, Fluorescence, Ribonucleoproteins, symbols, Biological system
Abstract

In the context of microbiology, recent studies show the importance of ribonucleo-protein aggregates (RNPs) for the understanding of mechanisms involved in cell responses to specific environmental conditions. The assembly and disassembly of aggregates is a dynamic process, the characterization of the stage of their evolution can be performed by the evaluation of their number. The aim of this study is to propose a method to automatically determine the count of RNPs. We show that the determination of a precise count is an issue by itself and hence, we propose three textural approaches: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view with Zernike moment descriptors (ZMD). These parameters are then used as inputs for a supervised classification in order to determine the most relevant. An experiment using a specific Saccharomyces cerevisiae strain presenting a fusion between a protein found in RNPs (PAB1) and the green fluorescent protein was performed to benchmark this approach. The fluorescence was observed with two-photon fluorescence microscopy. Results show that the textural approach, by mixing ZMD with Haralick features, allows for the characterization of the number of RNPs.

Published
2019
Full Text
View/download PDF

29. An Improvised k-NN Respecting Diversity of Data for Network Intrusion Detection

Author

Yasir Hamid, Sugumaran Muthukumarasamy, Balasaraswathi Ranganathan, Ludovic Journaux, Qaiser Farooq, Dept. of CSE Pondicherry Engineering College (CSE), CSE, Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] (Le2i), Université de Technologie de Belfort-Montbeliard (UTBM)-Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Department of Statistics, Pondicherry Central University, Université de Pondichéry, Dept. of CSE Pondicherry Engineering College ( CSE ), Laboratoire d'Electronique, d'Informatique et d'Image UMR CNRS 6306 ( Le2i ), Université de Technologie de Belfort-Montbeliard ( UTBM ) -Centre National de la Recherche Scientifique ( CNRS ) -École Nationale Supérieure d'Arts et Métiers ( ENSAM ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Pondicherry University

Subjects
Similarity (geometry), General Computer Science, Computer science, [ INFO.INFO-CR ] Computer Science [cs]/Cryptography and Security [cs.CR], 02 engineering and technology, Intrusion detection system, Gower metric, Reduction (complexity), Set (abstract data type), [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], 0202 electrical engineering, electronic engineering, information engineering, Point (geometry), Intrusion detection, Categorical variable, business.industry, General Engineering, 020206 networking & telecommunications, Pattern recognition, Euclidean distance, k - NN, Metric (mathematics), 020201 artificial intelligence & image processing, Artificial intelligence, business, Computer network, KDD’99
Abstract

Network Intrusion Detection is a complex classification problem aimed at discriminating the legitimate from illegitimate and potentially harmful network connections over the communication network. What adds to the complexity of the problem is the near real - time response to a threat, imbalanced datasets to deal with and finally the data being mixed in nature with some features being numeric some discrete an d some nominal. In this work we have applied Synthetic Minority Oversampling Technique ( SMOTE ) to balance the dataset and eliminate the skewness of the class distribution. The success of k - Nearest Neighbour ( k - NN ) depends upon the set of neighbours deemed to be very close or similar to a data point which is in turn determined by the similarity /distance metric empl oyed, where most of the metrics employed in literature deal with numeric data only, and either need con version of categorical features to numeric features or simply eliminated the categorical features, which often leads to reduction in the results. As for this work is considered, we take into consideration both the categories of features simultaneously by replac ing the conventional Euclidean metric with Gower metric, which is better suited for mixed data . Gower metric provides a mechanism to deal with heterogeneous features differently and ultimately yields a quantifiable value that determines the similarit y of the two instances. Experimental results show that improvised version of k - NN outperforms its conventional counterpart in terms of the Accuracy, Detection Rate, Precision, Recall, f - Measure, and Receiver Operating Characteristic ( ROC ) curve .

Published
2017
Full Text
View/download PDF

30. Trapping of carvacrol by konjac glucomannan-potato starch gels: Stability from macroscopic to microscopic scale, using image processing

Author

Jeannine Lherminier, Ludovic Journaux, Patricia Le Bail, Nathalie Cayot, John Aldo Lee, Aline Bonnotte, Céline Lafarge, Procédés Alimentaires et Microbiologiques [Dijon] (PAM), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Dispositif Inter-régional d'Imagerie Cellulaire [Dijon] (DImaCell), Procédés Alimentaires et Microbiologiques (PAM), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ingénierie et biologie cellulaire et tissulaire (IBCT (ex IFR133)), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Pôle d’Imagerie Médicale, Radiothérapie et Oncologie (MIRO), Université Catholique de Louvain = Catholic University of Louvain (UCL), Unité de recherche sur les Biopolymères, Interactions Assemblages (BIA), Institut National de la Recherche Agronomique (INRA), Procédés Alimentaires et Microbiologiques ( PAM ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Dispositif Inter-régional d'Imagerie Cellulaire [Dijon] ( DImaCell ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ingénierie et biologie cellulaire et tissulaire ( IBCT (ex IFR133) ), Centre Hospitalier Régional Universitaire [Besançon] ( CHRU Besançon ) -Etablissement français du sang [Bourgogne-France-Comté] ( EFS [Bourgogne-France-Comté] ) -Université de Franche-Comté ( UFC ) -Centre Hospitalier Régional Universitaire [Besançon] ( CHRU Besançon ) -Etablissement français du sang [Bourgogne-France-Comté] ( EFS [Bourgogne-France-Comté] ) -Université de Franche-Comté ( UFC ), Pôle d’Imagerie Médicale, Radiothérapie et Oncologie ( MIRO ), Université Catholique de Louvain ( UCL ), Unité de recherche sur les Biopolymères, Interactions Assemblages ( BIA ), Institut National de la Recherche Agronomique ( INRA ), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire [Besançon] (CHRU Besançon)-Etablissement français du sang [Bourgogne-France-Comté] (EFS [Bourgogne-France-Comté])-Université de Franche-Comté (UFC)-Centre Hospitalier Régional Universitaire [Besançon] (CHRU Besançon)-Etablissement français du sang [Bourgogne-France-Comté] (EFS [Bourgogne-France-Comté])-Université de Franche-Comté (UFC), and Université Catholique de Louvain (UCL)

Subjects
0106 biological sciences, Tapioca starch, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Freeze-thaw stability, Retrogradation (starch), Gelatinization, Starch, General Chemical Engineering, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, Retrogradation, 01 natural sciences, chemistry.chemical_compound, 0404 agricultural biotechnology, Image processing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Amylose, Carvacrol, 010608 biotechnology, Wheat-starch, [SDV.IDA]Life Sciences [q-bio]/Food engineering, medicine, Rheological properties, Potato starch, Xanthan gum, Konjac glucomannan, Chromatography, Syneresis, [ SDV.IDA ] Life Sciences [q-bio]/Food engineering, food and beverages, 04 agricultural and veterinary sciences, General Chemistry, 040401 food science, Dimensionality reduction, Cornstarch dispersions, chemistry, Chemical engineering, Aroma compounds, Amylopectin, Trapping, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Food Science, medicine.drug
Abstract

International audience; The aim of the study was to show that the presence of a small quantity of konjac glucomannan (KGM) in potato starch suspension increased the stability of carvacrol trapping.For that purpose, several freeze-thaw cycles were used to accelerate the ageing of the product and consequently drastically destabilize the gels, and induce syneresis. The stability of carvacrol trapping was evaluated by the quantification of carvacrol in the syneresis liquid. The stability of the starch gel structure was studied at microscopic and macroscopic scale.The moment of the addition of carvacrol and the presence of KGM both had an effect on the stability of carvacrol trapping and of the structure of the gel.KGM promoted amylose retrogradation but slowed down amylopectin retrogradation. The stability of potato starch gels can be improved by the addition of a small quantity of KGM which showed a 'cryo-protectant' behaviour.New method to characterize the micro and macrostructure from SEM images processing has also been proposed. The processing of microscopy images was done using Generalized Fourier Descriptors and allowed the characterization of each sample. The carvacrol addition lowered the physical stability of the gel with larger pores and increased syneresis. On the contrary, the KGM addition increased the size of the pores but prevented the formation of very large pores and reduced syneresis. The most stable system was obtained by the addition of carvacrol at the end of heating, in a konjac glucomannan potato starch gel.

Published
2017
Full Text
View/download PDF

31. Multidimensional Model Design using Data Mining: A Rapid Prototyping Methodology

Author

Bruno Faivre, Sandro Bimonte, Ludovic Journaux, Lucile Sautot, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne (UB), Etablissement Public Loire, DREAL Bassin Centre, Region Bourgogne (PARI, Projet Agrale 5), French Ministry of Agriculture, European Project: FEDER Loire ,FEDER Loire, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
Rapid prototyping, Data Warehouse, Uml Profile, Computer science, Evolution, 02 engineering and technology, computer.software_genre, Data Warehouses, Methodologies and Tools, 020204 information systems, Schema (psychology), 0202 electrical engineering, electronic engineering, information engineering, Data Mining, [INFO]Computer Science [cs], Conceptual-Model, OLAP, Online analytical processing, InformationSystems_DATABASEMANAGEMENT, Uml profile, Classification, Data warehouse, Multidimensional model, Support vector machine, Hardware and Architecture, 020201 artificial intelligence & image processing, Data mining, computer, Support-Vector-Machine, Software
Abstract

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE; International audience; Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.

Published
2017
Full Text
View/download PDF

32. Machine Learning Techniques for Intrusion Detection: A Comparative Analysis

Author

Ludovic Journaux, M. Sugumaran, Yasir Hamid, Dept. of CSE Pondicherry Engineering College (CSE), CSE, AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Dept. of CSE Pondicherry Engineering College ( CSE ), Laboratoire Electronique, Informatique et Image ( Le2i ), and Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS )

Subjects
Computer science, Anomaly-based intrusion detection system, 02 engineering and technology, Intrusion detection system, IDS, Machine learning, computer.software_genre, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Resource (project management), Component (UML), 0202 electrical engineering, electronic engineering, information engineering, ROC, Set (psychology), [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], False Positive, business.industry, ACM, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Precision, Object (computer science), True Positive, Outlier, 020201 artificial intelligence & image processing, The Internet, Artificial intelligence, Data mining, business, computer
Abstract

International audience; With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework " s security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.

Published
2016
Full Text
View/download PDF

33. A Fusion of Feature Extraction and Feature Selection Technique for Network Intrusion Detection

Author

Yasir Hamid, M. Sugumaran, Ludovic Journaux, Dept. of CSE Pondicherry Engineering College ( CSE ), CSE, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Dept. of CSE Pondicherry Engineering College (CSE), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
General Computer Science, Computer science, SVM, Feature extraction, [ INFO.INFO-CR ] Computer Science [cs]/Cryptography and Security [cs.CR], Feature selection, 02 engineering and technology, Intrusion detection system, Security policy, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Dimension (vector space), 0202 electrical engineering, electronic engineering, information engineering, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], PCA, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Pattern recognition, Intrusion Detection, Support vector machine, Principal component analysis, 020201 artificial intelligence & image processing, Artificial intelligence, business, Curse of dimensionality
Abstract

International audience; With varied and widespread attacks on information systems, intrusion detection systems (IDS) have become an indispensable part of security policy for protecting data. IDS monitor event logs and network traffic to uncover suspicious connections that deviate from the regular profile and identify them as threats or attacks. Like most of the cases the dataset used for intrusion detection i.e., KDD99 suffers two problems: imbalanced class distribution and curse of dimensionality. In this work SMOTE has been used for balancing the dataset and once balanced, Principal Component Analysis (PCA) has been used to extract the features. And after that on the transformed dataset Correlation based Feature Selection (CFS) is used to select a subset of important features. The reduced dimension dataset is tested with Support Vector Machines (SVM). Obtained results demonstrate improved detection accuracy, computational efficiency with minimal false alarms and less system resources utilization

Published
2016
Full Text
View/download PDF

34. Reliable Detection and Smart Deletion of Malassez Counting Chamber Grid in Microscopic White Light Images for Microbiological Applications

Author

Emmanuel Denimal, Stéphane Guyot, Ambroise Marin, Ludovic Journaux, Paul Molin, Service Systèmes d’Information - AgroSup Dijon, AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Groupe Modélisation Numérique en Agronomie et AgroAlimentaire - AgroSup Dijon ( Gmn3a ), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Procédés Alimentaires et Microbiologiques ( PAM ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Service d’Appui à la Recherche - AgroSup Dijon, Groupe Modélisation Numérique en Agronomie et AgroAlimentaire - AgroSup Dijon (Gmn3a), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Procédés Alimentaires et Microbiologiques (PAM), and Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
Microbiological Techniques, Microscopy, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Computer science, business.industry, Malassez, Grid, Set (abstract data type), Optics, Homomorphic filtering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, image analysis, noise filtering, Image Processing, Computer-Assisted, White light, Fourier transform, Segmentation, Computer vision, Artificial intelligence, homomorphic filtering, business, Instrumentation
Abstract

In biology, hemocytometers such as Malassez slides are widely used and are effective tools for counting cells manually. In a previous work, a robust algorithm was developed for grid extraction in Malassez slide images. This algorithm was evaluated on a set of 135 images and grids were accurately detected in most cases, but there remained failures for the most difficult images. In this work, we present an optimization of this algorithm that allows for 100% grid detection and a 25% improvement in grid positioning accuracy. These improvements make the algorithm fully reliable for grid detection. This optimization also allows complete erasing of the grid without altering the cells, which eases their segmentation.

Published
2015
Full Text
View/download PDF

35. Comparison of leaf surface roughness analysis methods by sensitivity to noise analysis

Author

Houda Bediaf, Ludovic Journaux, Frédéric Cointault, Rachid Sabre, AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Nicephore Cite, and Burgundy Region

Subjects
Surface (mathematics), Materials science, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Gaussian, Soil Science, Wavelet decomposition, Surface finish, Leaf roughness, Noise analysis, symbols.namesake, Optics, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Surface roughness, Sensitivity (control systems), Generalized Fourier Descriptors, Sensitivity indicator, business.industry, Optical roughness, Noise, Control and Systems Engineering, symbols, Wetting, Biological system, business, Agronomy and Crop Science, Intensity (heat transfer), Food Science
Abstract

International audience; Surface roughness is of great interest in agricultural spraying because it is used to characterise leaf surface wettability to predict the behaviour of droplets on a leaf surface. In recent years, the use of texture analysis to estimate surface roughness has emerged. In this paper we propose to estimate leaf surface roughness by using an optimisation of the Generalized Fourier Descriptors method. This approach is then compared with two other standard methods in the literature, one based on grey level intensity variation and the other on wavelet decomposition. Since roughness has many definitions and each method is calculated differently, we propose a new approach to compare the results based on the sensitivity of each method according to surface roughness variations. These variations were introduced by adding different kinds of noise to the image. Gaussian and salt & pepper noise are added to simulate rapid changes and peak impulses on the surface topography, whereas a Structural noise (sinusoidal signal) is added to simulate depth on the surface topography. The novelty of this contribution is the use of a new approach and procedure for agronomic application (leaf surface roughness). The results obtained are expected to be used to characterise the adhesion mechanisms of liquid droplets on a leaf surface.

Published
2015
Full Text
View/download PDF

36. Efficient unsupervised clustering for spatial bird population analysis along the Loire river

Author

Aurore Payen, Ludovic Journaux, Clément Delion, Lucile Sautot, Bruno Faivre, AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), AgroParisTech, Biogéosciences [Dijon] ( BGS ), Michel Verleysen, Journaux, Ludovic, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Biogéosciences [UMR 6282] [Dijon] (BGS), and Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)

Subjects
Clustering Algorithms, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Nonlinear dimension reduction, Multiscale Jensen-Shannon Embedding, Dimension Reduction, Loire River
Abstract

International audience; This paper focuses on application and comparison of Non Linear Dimensionality Reduction (NLDR) methods on natural high dimensional bird communities dataset along the Loire River (France). In this context, biologists usually use the well-known PCA in order to explain the upstream-downstream gradient.Unfortunately this method was unsuccessful on this kind of nonlinear dataset.The goal of this paper is to compare recent NLDR methods coupled with different data transformations in order to find out the best approach. Results show that Multiscale Jensen-Shannon Embedding (Ms JSE) outperform all over methods in this context.

Published
2015

37. Une nouvelle approche mixte d'enrichissement de dimensions dans un schéma multidimensionnel en constellation Application à la biodiversité des oiseaux

Author

Lucile Sautot, Sandro Bimonte, Ludovic Journaux, Bruno Faivre, Journaux, Ludovic, AgroParisTech, Biogéosciences [UMR 6282] [Dijon] (BGS), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), and Laboratoire Electronique, Informatique et Image ( Le2i )

Subjects
Multidimensional design, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SDE] Environmental Sciences, [ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR], Data Warehouse, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], OLAP, Biodiversity, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [ SDE ] Environmental Sciences, [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [SDE]Environmental Sciences, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Data mining
Abstract

International audience; Les entrepôts de données (DW) et les systèmes OLAP sont des technologies d'analyse en ligne pour de grands volumes de données, basés sur les be-soins des utilisateurs. Leur succès dépend essentiellement de la phase de conception où les exigences fonctionnelles sont confrontées aux sources de données (méthodologie de conception mixte). Cependant, les méthodes de conception existantes semblent parfois inefficaces, lorsque les décideurs définissent des exi-gences fonctionnelles qui ne peuvent être déduites à partir des sources de don-nées (approche centrée sur les données), ou lorsque le décideur n'a pas intégré tous ces besoins durant la phase de conception (approche centrée sur l'utilisa-teur). Cet article propose une nouvelle méthodologie mixte d'enrichissement de schémas en constellation, où l'approche classique de conception est améliorée grâce à la fouille de données dans le but de créer de nouvelles hiérarchies au sein d'une dimension. Un prototype associé est également présenté.

Published
2015

38. The Hierarchical Agglomerative Clustering with Gower index: a methodology for automatic design of OLAP cube in ecological data processing context

Author

Bruno Faivre, Ludovic Journaux, Lucile Sautot, Paul Molin, Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Electronique, Informatique et Image ( Le2i ), Procédés Alimentaires et Microbiologiques ( PAM ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Biogéosciences [UMR 6282] [Dijon] (BGS), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Département des Sciences de l'Ingénieur et des Procédés (DSIP), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, AgroParisTech, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Département des Sciences de l'Ingénieur et des Procédés ( DSIP )

Subjects
[ INFO.INFO-NA ] Computer Science [cs]/Numerical Analysis [cs.NA], Computer science, Context (language use), 02 engineering and technology, computer.software_genre, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Dimension (data warehouse), Cluster analysis, Ecology, Evolution, Behavior and Systematics, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], OLAP, Ecology, Automatic design, Applied Mathematics, Ecological Modeling, Online analytical processing, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], InformationSystems_DATABASEMANAGEMENT, Hierarchical agglomerative clustering, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Missing data, Data warehouse, Computer Science Applications, Hierarchical clustering, [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], Computational Theory and Mathematics, Modeling and Simulation, OLAP cube, 020201 artificial intelligence & image processing, Data mining, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Bird population, computer
Abstract

In Press, Corrected Proof; International audience; The OLAP systems can be an improvement for ecological studies. In fact, ecology studies, follows and analyzes phenomenon across space and time and according to several parameters. OLAP systems can provide to ecologists browsing in a large dataset. One focus of the current research on OLAP system is the automatic design of OLAP cubes and of data warehouse schemas. This kind of works makes accessible OLAP technology to non information technology experts. But to be efficient, the automatic OLAP building must take into account various cases. Moreover the OLAP technology is based on the concept of hierarchy. Thereby the hierarchical clustering methods are often used by OLAP system designer. In this article, we propose using hierarchical agglomerative clustering with a metric that comes from ecological studies (the Gower similarity index) to build automatically hierarchical dimensions in an OLAP cube. With this similarity index we can perform a hierarchical clustering on heterogeneous datasets that contains qualitative and quantitative variables. We offer a prototypical automatic system which builds dimension for an OLAP cube and we measure the performances of this system according to the number of clustered individuals and according to the number of variables used for clustering. Thanks to these measures we can offer an approximation of performances with a large dataset. Thereby the Gower index in a hierarchical agglomerative clustering permits the management of heterogeneous dataset with missing values in a context of automatic building of OLAP cube. With this methodology, we can build new dimensions based on hierarchies in the data, which are not evident. The data mining methods can complete the expert knowledge during the design of an OLAP cube, because these methods can explain the inherent structure of the data.

Published
2014
Full Text
View/download PDF

39. Model and Data Engineering: 4th International Conference, MEDI 2014, Larnaca, Cyprus, September 24-26, 2014. Proceedings

Author

Bruno Faivre, Ludovic Journaux, Sandro Bimonte, Lucile Sautot, Assistance à la Certification d’Applications DIstribuées et Embarquées (IRIT-ACADIE), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire d'Informatique et d'Automatique pour les Systèmes (LIAS), Université de Poitiers-ENSMA, University of Cyprus (UCY), Ait Ameur, Yamine, Bellatreche, Ladjel, and Papadopoulos, George Angelos

Subjects
060201 languages & linguistics, Rapid prototyping, Computer science, Online analytical processing, InformationSystems_DATABASEMANAGEMENT, 06 humanities and the arts, 02 engineering and technology, computer.software_genre, Data warehouse, Schema (psychology), 0602 languages and literature, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Data mining, computer
Abstract

Data Warehouses (DWs) are large repositories of data aimed at supporting the decision-making process by enabling flexible and interactive analyses via OLAP systems. Rapid prototyping of DWs is necessary when OLAP applications are complex. Some work about the integration of Data Mining and OLAP systems has been done to enhance OLAP operators with mined indicators, and/or to define the DW schema. However, to best of our knowledge, prototyping methods for DWs do not support this kind of integration. Then, in this paper we present a new prototyping methodology for DWs, extending [3], where DM methods are used to define the DW schema. We validate our approach on a real data set concerning bird biodiversity.

Published
2014
Full Text
View/download PDF

40. Construction de Modèles Prédictifs pour l'Analyse des Relations Oiseaux-Paysage

Author

Lucile Sautot, Bruno Faivre, Jean-Claude Simon, Ludovic Journaux, Léo Perriche, Biogéosciences [UMR 6282] [Dijon] (BGS), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Journaux, Ludovic, Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Laboratoire Electronique, Informatique et Image ( Le2i )

Subjects
[ SDV.BID ] Life Sciences [q-bio]/Biodiversity, [SPI]Engineering Sciences [physics], relations espèces-environnement, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SPI] Engineering Sciences [physics], [ SPI ] Engineering Sciences [physics], oiseaux, data mining, [SDV.BID]Life Sciences [q-bio]/Biodiversity, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [SDV.BID] Life Sciences [q-bio]/Biodiversity, modélisation
Abstract

National audience; Cet article présente une comparaison de trois méthodes (Modèles Linéaires Généralisés, Réseaux de Neurones, Machines Vecteurs Supports) et de différentes combinaisons de prétraitements de données (filtrage, arrondi, analyse factorielle, sélection de paramètres). L'objectif de cette comparaison est de définir quel est le processus qui permet de construire le meilleur modèle prédictif, dans le cadre de la prédiction d'abondances d'espèces d'oiseaux à partir de variables décrivant le paysage. Nous comparerons les modèles grâce à l'erreur moyenne absolue et à l'information mutuelle. Cette comparaison a montré qu'aucune technique étudiée ne permet de construire des modèles prédictifs très précis, mais que des réseaux de neurones appliqués sur des données filtrés permettent de modéliser les zones de présence et d'absence des espèces.

Published
2013

41. Roughness evaluation of vine leaf by image processing

Author

Agrosup Dijon, Ludovic Journaux, Rachid Sabre, Houda Bediaf, Frédéric Cointault, Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Centre Scientifique et Technique du Bâtiment (CSTB), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, sabre, rachid, Centre Scientifique et Technique du Bâtiment ( CSTB ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Journaux, Ludovic, Laboratoire Electronique, Informatique et Image ( Le2i ), and Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS )

Subjects
[ MATH ] Mathematics [math], 0106 biological sciences, 0209 industrial biotechnology, Scanning electron microscope, [SDV]Life Sciences [q-bio], Computer Vision, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, [MATH] Mathematics [math], 02 engineering and technology, Surface finish, Leaf roughness, 01 natural sciences, [PHYS] Physics [physics], [SPI]Engineering Sciences [physics], 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [ SPI ] Engineering Sciences [physics], Surface roughness, Computer vision, [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS, [PHYS]Physics [physics], [ PHYS ] Physics [physics], Artificial neural network, [STAT]Statistics [stat], Multilayer perceptron, [SDE]Environmental Sciences, Biological system, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Materials science, [ STAT ] Statistics [stat], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SPI] Engineering Sciences [physics], IASTED, Fast Fourier transform, Neural Network, Image processing, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Texture, language technologies, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Precision agriculture, business.industry, [STAT] Statistics [stat], Artificial intelligence, business, 010606 plant biology & botany
Abstract

International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The system for image acquisition of leaf surface consists of scanning electron microscope (SEM). The images of leaf surface are captured and analyzed to estimate the optical roughness. 2-D Fast Fourier Trans- form (FFT) algorithm and Co-occurrence Matrix are used for texture analysis. A multilayer perceptron (MLP) neural network is used to model and predict the optical roughness values.

Published
2013
Full Text
View/download PDF

42. 3D image acquisition system based on shape from focus technique

Author

Ludovic Journaux, Jean-Claude Simon, Pierre Gouton, Bastien Billiot, Frédéric Cointault, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Regional Council of Burgundy, Development pole Nicephore Cite based at Chalon-sur-Saone in France, Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), and Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
Engineering, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, [SDV]Life Sciences [q-bio], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, scenes, Image processing, agronomic scenes, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 02 engineering and technology, Classification of discontinuities, lcsh:Chemical technology, Biochemistry, Article, Analytical Chemistry, Domain (software engineering), shape from focus, Depth map, 0202 electrical engineering, electronic engineering, information engineering, agronomic, lcsh:TP1-1185, Computer vision, 3D image acquisition system, focus measure, Depth of field, Electrical and Electronic Engineering, Instrumentation, ComputingMethodologies_COMPUTERGRAPHICS, Monocular, [ SDV ] Life Sciences [q-bio], business.industry, Scene statistics, Distributed object, 021001 nanoscience & nanotechnology, Atomic and Molecular Physics, and Optics, 020201 artificial intelligence & image processing, Artificial intelligence, 0210 nano-technology, business
Abstract

agent Agrosup Dijon de l'UMREcolDurGEAPSI; This paper describes the design of a 3D image acquisition system dedicated to natural complex scenes composed of randomly distributed objects with spatial discontinuities. In agronomic sciences, the 3D acquisition of natural scene is difficult due to the complex nature of the scenes. Our system is based on the Shape from Focus technique initially used in the microscopic domain. We propose to adapt this technique to the macroscopic domain and we detail the system as well as the image processing used to perform such technique. The Shape from Focus technique is a monocular and passive 3D acquisition method that resolves the occlusion problem affecting the multi-cameras systems. Indeed, this problem occurs frequently in natural complex scenes like agronomic scenes. The depth information is obtained by acting on optical parameters and mainly the depth of field. A focus measure is applied on a 2D image stack previously acquired by the system. When this focus measure is performed, we can create the depth map of the scene.

Published
2013
Full Text
View/download PDF

43. Noise Robustness of a Texture Classification Protocol for Natural Leaf Roughness Characterisation

Author

Johel Miteran, Jean-Claude Simon, Ludovic Journaux, Thomas Decourselle, Frédéric Cointault, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Laboratoire Electronique, Informatique et Image ( Le2i ), and Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS )

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Computer science, Word error rate, 02 engineering and technology, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, robustness, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Scaling, dimensionality reduction, business.industry, Dimensionality reduction, 020206 networking & telecommunications, Pattern recognition, informa- tion losses, Fourier transform, symbols, 020201 artificial intelligence & image processing, texture classification, Artificial intelligence, business, Classifier (UML), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Curse of dimensionality
Abstract

International audience; In the context of leaf roughness study for precision spray- ing applications, this article deals with its characterisation by computer vision techniques. Texture analysis is a pri- mordial step for applications based on image analysis such as medical or agronomical imaging. The aim is to classify textures after extraction of discriminating features. How- ever, this problem remains complex in the case of natural leaves because of changes in lighting, scaling or orienta- tion. There we consider a family of invariants from the frequency domain called Generalized Fourier Descriptors whose dimensionality is proportional to the spatial resolu- tion of the images. These features used with a Support Vec- tor Machines classifier lead to good results in terms of clas- sification error rate when the dimensionality is small but it gives more errors when the dimensionality increases; we use there different kinds of dimensionality reduction tech- niques (linear or non-linear) whose aim is to keep most in- formation in a vector of small dimensionality. It implies losses of information even if small. This is not the only source of losses, another one is related to the noise present in the images due to acquisition conditions and sensor sen- sitivity. We propose here to demonstrate the robustness of our method of classification despite these losses of infor- mation.

Published
2012
Full Text
View/download PDF

44. Texture, color and frequential proxy-detection image processing for crop characterization in a context of precision agriculture

Author

Gilbert Grenier, Olivier Lavialle, Frédéric Cointault, Nathalie Gorretta, Ludovic Journaux, David Ooms, Marie-France Destain, Gilles Rabatel, Ambroise Marin, Christian Germain, Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université de Liège, Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Information – Technologies – Analyse Environnementale – Procédés Agricoles ( UMR ITAP ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Laboratoire de l'intégration, du matériau au système ( IMS ), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique ( CNRS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1

Subjects
2. Zero hunger, 0106 biological sciences, AGRICULTURE, Image processing, 04 agricultural and veterinary sciences, PROXI DETECTION, 15. Life on land, Weed detection, 01 natural sciences, [ SDE ] Environmental Sciences, CAPTEUR OPTIQUE, Geography, DONNEE D'IMAGE, ANALYSE DE TEXTURE, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Image acquisition, Precision agriculture, IMAGE HYPERSPECTRALE, Crop management, AGRICULTURE DE PRECISION, CARACTERISATION, 010606 plant biology & botany, Remote sensing
Abstract

Le concept d’agriculture de précision consiste à gérer spatialement les pratiques culturales en fonction des variabilités intra-parcellaires. La plupart des outils associés utilisent des capteurs optiques ou imageurs. L’imagerie aérienne est notamment souvent utilisée pour compléter ou remplacer des capteurs embarqués sur machine agricole, mais elle présente des inconvénients comparée aux capteurs optiques de " proxi-détection " : résolution, accessibilité, coût, temporalité, etc. Nous proposons dans ce chapitre d’explorer le domaine de la proxi-détection en s’intéressant d’abord au développement de systèmes d’acquisition d’image robustes, puis aux méthodes de traitement d’image à travers divers exemples d’applications : caractérisation de cultures de blé d’une part (détection/comptage d’épis, détection d’adventices), évolution de la germination de graines (chicorée) d’autre part. / The concept of precision agriculture consists to spatially manage crop management practices according to in-field variability. Most of the tools used for Precision Farming utilize optical and/or imaging sensors. Remote sensing imaging devices are often used to complete or replace embedded sensors onboard the agricultural machinery, however they get some drawbacks compared to “proxy-detection” optical sensors: resolution, accessibility, cost, temporality, etc. We propose in this chapter to explore the proxi-detection domain by focusing first on the development of robust image acquisition systems, and secondly on the use of image processing through different application examples dealing with wheat crop characterization (detection and counting of wheat ears, weed detection), and evaluation of seed development/germination performance of chicory achenes.

Published
2012

45. Plant Leaf Roughness Analysis by Texture Classification with Generalized Fourier Descriptors in different Dimensionality Reduction context

Author

Marie-France Destain, Alexis Piron, Ludovic Journaux, Frédéric Cointault, Jean-Claude Simon, Johel Miteran, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Gembloux Agro-Biotech, Université de Liège - Gembloux, Département des Sciences de l'Ingénieur et des Procédés (DSIP), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), and Département des Sciences de l'Ingénieur et des Procédés ( DSIP )

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, SVM, Feature extraction, fourier descriptor, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 02 engineering and technology, Surface finish, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Mathematics, business.industry, Dimensionality reduction, Nonlinear dimensionality reduction, 020206 networking & telecommunications, Pattern recognition, Support vector machine, Fourier transform, classification, symbols, 020201 artificial intelligence & image processing, Artificial intelligence, Kernel Fisher discriminant analysis, General Agricultural and Biological Sciences, business, Merge (version control), texture, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In the context of plant leaf roughness analysis for precision spraying, this study explores the capability and the performance of some combinations of pattern recognition and computer vision techniques to extract the roughness feature. The techniques merge feature extraction, linear and nonlinear dimensionality reduction techniques, and several kinds of methods of classification. The performance of the methods is evaluated and compared in terms of the error of classification. The results for the characterization of leaf roughness by generalized Fourier descriptors for feature extraction, kernel-based methods such as support vector machines for classification and kernel discriminant analysis for dimensionality reduction were encouraging. These results pave the way to a better understanding of the adhesion mechanisms of droplets on leaves that will help to reduce and improve the application of phytosanitary products and lead to possible modifications of sprayer configurations

Published
2011
Full Text
View/download PDF

46. Impact of oxidoreduction potential and of gas bubbling on rheological properties of non-fat yoghurt

Author

F. Martin, Ludovic Journaux, Patrick Gervais, C. Vergoignan, Rémy Cachon, Nathalie Cayot, Génie des Procédés Microbiologiques et Alimentaires (GPMA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, FLAveur, VIsion et Comportement du consommateur (FLAVIC), Etablissement National d'Enseignement Supérieur Agronomique de Dijon (ENESAD)-Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), and AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
LACTOBACILLUS DELBRUECKII SUBSP. BULGARIS, GEL STRUCTURE, Viscosity, chemistry.chemical_compound, STREPTOCOCCUS THERMOPHILUS, 0404 agricultural biotechnology, Rheology, EXOPLYSACCHARIDES, Oxidizing agent, [SDV.IDA]Life Sciences [q-bio]/Food engineering, OXIREDUCTION POTENTIAL, Food science, NON-FAT YOGHURT, biology, Chemistry, 0402 animal and dairy science, food and beverages, 04 agricultural and veterinary sciences, Lactobacillaceae, Apparent viscosity, biology.organism_classification, Microstructure, 040401 food science, 040201 dairy & animal science, Lactic acid, Lactobacillus delbrueckii subsp. bulgaricus, Food Science
Abstract

International audience; The aim of this work was to study the effect of different gaseous conditions on the physico-chemical properties of yoghurt. Four conditions of oxidoreduction potential (Eh7), +433 mV (milk gassed with air), +405 mV (ungassed milk), +283 mV (milk gassed with N2) and −349 mV (milk gassed with N2–H2) were applied to milk. The rheological properties and microstructure of these yoghurts were determined by measuring apparent viscosity, whey separation (WS) and using confocal laser scanning microscopy. Exopolysaccharide (EPS) production by lactic acid bacteria (LAB) was also studied, and production was increased for extreme Eh7 values: the highest values were obtained for air (oxidizing condition) and for N2–H2 (reducing condition). Yoghurts made under bubbled N2–H2 clearly led to low WS with higher gel aggregation and a lower proportion of pores.

Published
2010
Full Text
View/download PDF

47. Effect of oxidoreduction potential and of gas bubbling on rheological properties and microstructure of acid skim milk gels acidified with glucono-delta-lactone

Author

Philippe Cayot, F. Martin, Ludovic Journaux, Nathalie Cayot, Patrick Gervais, Rémy Cachon, Ambroise Marin, FLAveur, VIsion et Comportement du consommateur ( FLAVIC ), Etablissement National d'Enseignement Supérieur Agronomique de Dijon ( ENESAD ) -Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne ( UB ), FLAveur, VIsion et Comportement du consommateur (FLAVIC), Etablissement National d'Enseignement Supérieur Agronomique de Dijon (ENESAD)-Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), and Université de Bourgogne (UB)

Subjects
food.ingredient, Microorganism, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, Gluconates, Gluconolactone, Matrix (chemical analysis), Lactones, 0404 agricultural biotechnology, food, Rheology, Skimmed milk, Genetics, Confocal laser scanning microscopy, Food and Nutrition, Animals, Food science, Chemistry, 0402 animal and dairy science, food and beverages, 04 agricultural and veterinary sciences, Hydrogen-Ion Concentration, Microstructure, 040401 food science, 040201 dairy & animal science, Milk, Chemical engineering, Alimentation et Nutrition, Food Technology, Animal Science and Zoology, Gases, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Oxidation-Reduction, δ lactone, Food Science
Abstract

Milk oxidoreduction potential was modified using gases during the production of a model dairy product and its effect on gel setting was studied. Acidification by glucono-delta-lactone was used to examine the physicochemistry of gelation and to avoid variations due to microorganisms sensitive to oxidoreduction potential. Four conditions of oxidoreduction potential were applied to milk: milk was gassed with air, nongassed, gassed with N(2), or gassed with N(2)H(2). The rheological properties and microstructure of these gels were determined using viscoelasticimetry, measurement of whey separation, and confocal laser scanning microscopy. It appeared that a reducing environment led to less-aggregated proteins within the matrix and consequently decreased whey separation significantly. The use of gas to modify oxidoreduction potential is a possible way to improve the quality of dairy products.

Published
2009
Full Text
View/download PDF

48. Reduction of the number of spectral bands in LANDSAT images with projection methods: Pertinence of the resulting Information

Author

Irène Foucherot, Pierre Gouton, and Ludovic Journaux

Subjects
Reduction (complexity), Multispectral image processing, Geography, Principal component analysis, Télédétection, imagerie multispectrale, méthodes de projection, segmentation, Remote sensing, multispectral image processing, projection methods, segmentation, Spectral bands, Projection (set theory), Algorithm, Linear methods, Non lineaire
Abstract

Dans cet article, nous presentons des techniques de projection de donnees, lineaires et non lineaires, que nous avons utilisees afin de reduire le nombre de bandes spectrales a traiter dans des images multispectrales de type Landsat. Les methodes presentees sont l'Analyse en Composantes Curvilignes (ACC, methode non lineaire), ainsi qu'une optimisation de celle-ci (appelee ACCinitACP) obtenue a partir de l'Analyse en Composantes Principales (ACP, methode lineaire). Afin d'evaluer la pertinence de l'information restante apres reduction du nombre de bandes spectrales, nous avons applique une segmentation sur les images originales et sur les images reduites par chaque methode de projection. Nous avons ainsi pu constater que l'organisation du paysage est preservee par ces transformations. Cet article montre en outre les resultats suivants : l'ACC est un outil tres interessant pour le traitement des images multispectrales ; l'ACC peut reellement etre consideree comme une extension non-lineaire de l'ACP ; l'ACCinitACP donne des resultats identiques a ceux de l'ACC tout en reduisant les temps de calculs de la transformation.. This paper describes applications of linear and nonlinear projection methods, in order to obtain a reduction of the number of spectral bands in LANDSAT multispectral images. We present Curvilinear Component Analysis (CCA, nonlinear method) and an optimisation of it based on the use of Principal Component Analysis (PCA, linear method). In order to evaluate the pertinence of the information kept by each transformation, we then apply segmentation on the transformed and original images. This processing allows us to show that the structure (the landscape organization) of the image is preserved by each transformation. This paper tends to show several results: CCA is an improvement for dimensions reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimisation through PCA (called CCAinitPCA) allows a reduction of the calculations, providing a result identical to that of CCA.. Keywords : Teledetection, imagerie multispectrale, methodes de projection, segmentation; Remote sensing, multispectral image processing, projection methods, segmentation.. Journal des Sciences Pour l\'Ingenieur. Vol. 7 2006: pp. 10-19

Published
2008
Full Text
View/download PDF

49. Statistical methods for texture analysis applied to agronomical images

Author

Pierre Gouton, Frédéric Cointault, and Ludovic Journaux

Subjects
business.industry, Feature extraction, Pattern recognition, Image processing, Image segmentation, Texture (music), Class (biology), Image (mathematics), Image texture, Cluster validity index, Computer vision, Artificial intelligence, business, Mathematics
Abstract

For activities of agronomical research institute, the land experimentations are essential and provide relevant information on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture image processing to estimate the number of ears per square metre can be an improvement. Then, different image segmentation techniques based on feature extraction have been tested using textural information with first and higher order statistical methods. The Run Length method gives the best results closed to manual countings with an average error of 3%. Nevertheless, a fine justification of hypothesis made on the values of the classification and description parameters is necessary, especially for the number of classes and the size of analysis windows, through the estimation of a cluster validity index. The first results show that the mean number of classes in wheat image is of 11, which proves that our choice of 3 is not well adapted. To complete these results, we are currently analysing each of the class previously extracted to gather together all the classes characterizing the ears.

Published
2008
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results