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11. New foreground markers for Drosophila cell segmentation using marker-controlled watershed

Author

Rim Rahali, Yassine Ben Salem, Noura Dridi, and Hassen Dahman

Subjects
Segmentation, General Computer Science, Drosophila cells, Marker controlled watershed, Foreground markers, Electrical and Electronic Engineering, Fiji software, Obj.MPP framework
Abstract

Image segmentation consists of partitioning the image into different objects of interest. For a biological image, the segmentation step is important to understand the biological process. However, it is a challenging task due to the presence of different dimensions for cells, intensity inhomogeneity, and clustered cells. The marker-controlled watershed (MCW) is proposed for segmentation, outperforming the classical watershed. Besides, the choice of markers for this algorithm is important and impacts the results. For this work, two foreground markers are proposed: kernels, constructed with the software Fiji and Obj.MPP markers, constructed with the framework Obj.MPP. The new proposed algorithms are compared to the basic MCW. Furthermore, we prove that Obj.MPP markers are better than kernels. Indeed, the Obj.MPP framework takes into account cell properties such as shape, radiometry, and local contrast. Segmentation results, using new markers and illustrated on real Drosophila dataset, confirm the good performance quality in terms of quantitative and qualitative evaluation.

Published
2022
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13. B-Spline Level Set For Drosophila Image Segmentation

Author

H. Dahman, Rim Rahali, Yassine Ben Salem, and Noura Dridi

Subjects
Level set (data structures), Level set method, Implicit function, Basis (linear algebra), business.industry, B-spline, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Image segmentation, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Sørensen–Dice coefficient, Computer Science::Computer Vision and Pattern Recognition, 030220 oncology & carcinogenesis, Segmentation, Artificial intelligence, business
Abstract

Segmentation of biological images is a challenging task, due to non convex shapes, intensity inhomogeneity and clustered cells. To address these issues, a new algorithm is proposed based on the B-spline level set method. The implicit function of the level set is modelled as a continuous parametric function represented with the B-spline basis. It is different from the discrete formulation associated with conventional level set. In this paper the proposed framework takes into account properties of biological images. The algorithm is applied to Drosophila images, and compared to conventional level set and Marker Controlled Watershed (MCW). Results show good performance in term of the DICE coefficient, for noisy and noiseless images.

Published
2020
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14. Akaike and Bayesian Information Criteria for Hidden Markov Models

Author

Melita Hadzagic and Noura Dridi

Subjects
Statistics::Theory, Statistics::Applications, Computer science, Applied Mathematics, Model selection, 020206 networking & telecommunications, 02 engineering and technology, Bayesian information criterion, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Bit error rate, Statistics::Methodology, Electrical and Electronic Engineering, Akaike information criterion, Hidden Markov model, Algorithm, Independence (probability theory), Communication channel
Abstract

We propose the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for model selection in hidden Markov models (HMM) when the number of states is unknown. The exact solutions exploit the properties of HMM that allow tractable forms of both criteria to be obtained while transgressing the common assumption in AIC and BIC model selection approaches on the independence of data. The proposed algorithm is presented and evaluated in application to blind channel estimation and symbol detection when the channel length is assumed unknown.

Published
2019
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15. Bayesian inference for biomarker discovery in proteomics: an analytic solution

Author

Patrick Ducoroy, Bruno Lacroix, Laurent Gerfault, Melita Hadzagic, Pascal Roy, Jean-Philippe Charrier, Pierre Grangeat, Audrey Giremus, Jean-François Giovannelli, Noura Dridi, Caroline Truntzer, 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 ), Plate-forme Protéomique CLIPP - Clinical and Innovation Proteomic Platform [Dijon] ( CLIPP ), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies ( FEMTO-ST ), Université de Technologie de Belfort-Montbeliard ( UTBM ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ) -Université de Technologie de Belfort-Montbeliard ( UTBM ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ) -Institut de Chimie Moléculaire de l'Université de Bourgogne [Dijon] ( ICMUB ), Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ), Recherche Technologique, bioMerieux SA, BIOMERIEUX, Laboratoire d'Electronique et des Technologies de l'Information ( CEA-LETI ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Grenoble Alpes [Saint Martin d'Hères], Biomérieux Lyon, Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de l'intégration, du matériau au système (IMS), Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1, Plate-forme Protéomique CLIPP - Clinical and Innovation Proteomic Platform [Dijon] (CLIPP), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie Moléculaire de l'Université de Bourgogne [Dijon] (ICMUB), Université de Bourgogne (UB)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Institut de Chimie Moléculaire de l'Université de Bourgogne [Dijon] (ICMUB), Service de Biostatistiques [Lyon], Hospices Civils de Lyon (HCL), and Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Proteomics, Optimal decision, Variable selection, Computer science, Posterior probability, Bayesian probability, Feature selection, Bayesian inference, Machine learning, computer.software_genre, Model selection, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, 010104 statistics & probability, 03 medical and health sciences, Lasso (statistics), [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], 0101 mathematics, Biomarker discovery, Evidence, Covariance matrix, business.industry, Research, Bayesian approach, Biomarker, Computer Science Applications, Computational Mathematics, 030104 developmental biology, [ SDV.BBM.GTP ] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Algorithm, Hierarchical model
Abstract

International audience; This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought. In this way, correlations between the proteins are intrinsically taken into account in the decision. The developed strategy is derived in a Bayesian setting, and the decision is optimal in the sense that it minimizes a global mean error. It is finally based on the posterior probabilities of the partitions. The main difficulty is to calculate these probabilities since they are based on the so-called evidence that require marginalization of all the unknown model parameters. Two models are presented that relate the status to the protein concentrations, depending whether the latter are biomarkers or not. The first model accounts for biological variabilities by assuming that the concentrations are Gaussian distributed with a mean and a covariance matrix that depend on the status only for the biomarkers. The second one is an extension that also takes into account the technical variabilities that may significantly impact the observed concentrations. The main contributions of the paper are: (1) a new Bayesian formulation of the biomarker selection problem, (2) the closed-form expression of the posterior probabilities in the noiseless case, and (3) a suitable approximated solution in the noisy case. The methods are numerically assessed and compared to the state-of-the-art methods (t test, LASSO, Battacharyya distance, FOHSIC) on synthetic and real data from proteins quantified in human serum by mass spectrometry in selected reaction monitoring mode.

Published
2017
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16. VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS

Author

L. Gerfaut, Patrick Ducoroy, Pierre Grangeat, Catherine Mercier, Pascal Roy, Jean-Philippe Charrier, Jean-François Giovannelli, Audrey Giremus, Noura Dridi, Caroline Truntzer, 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 ), Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de l'Accélérateur Linéaire ( LAL ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Electronique et des Technologies de l'Information ( CEA-LETI ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Grenoble Alpes [Saint Martin d'Hères], Bio-Mérieux [Marcy l'Etoile], BIOMERIEUX, Plate-forme Protéomique CLIPP - Clinical and Innovation Proteomic Platform [Dijon] ( CLIPP ), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies ( FEMTO-ST ), Université de Technologie de Belfort-Montbeliard ( UTBM ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ) -Université de Technologie de Belfort-Montbeliard ( UTBM ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ) -Institut de Chimie Moléculaire de l'Université de Bourgogne [Dijon] ( ICMUB ), Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bourgogne ( UB ) -Centre National de la Recherche Scientifique ( CNRS ), Service de Biostatistique des Hospices Civils de Lyon, Hospices Civils de Lyon ( HCL ), DRIDI, Noura, Laboratoire de l'intégration, du matériau au système (IMS), Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1, Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Accélérateur Linéaire (LAL), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Sud - Paris 11 (UP11), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Plate-forme Protéomique CLIPP - Clinical and Innovation Proteomic Platform [Dijon] (CLIPP), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie Moléculaire de l'Université de Bourgogne [Dijon] (ICMUB), Université de Bourgogne (UB)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Institut de Chimie Moléculaire de l'Université de Bourgogne [Dijon] (ICMUB)

Subjects
0209 industrial biotechnology, business.industry, Computer science, Instrumental variable, Posterior probability, Bayesian probability, Pattern recognition, Feature selection, 02 engineering and technology, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Logistic regression, 01 natural sciences, 010104 statistics & probability, 020901 industrial engineering & automation, Cohort, Probability distribution, Bayesian hierarchical modeling, Artificial intelligence, 0101 mathematics, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Selection (genetic algorithm), [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (1) we do not impose ad-hoc relationships between the variables such as a logistic regression model and (2) we account for instrumental variability through measurement noise. We are then dealing with indirect observations of a mixture of distributions and it results in intricate probability distributions. A closed-form expression of the posterior distributions cannot be derived. Thus, we discuss several approximations and study the robustness to the noise level. Finally, the method is evaluated both on simulated and clinical data. Index Terms— Model and variable selection, Bayesian approach, biological et technological variability, Gaussian mixture, proteomics.

Published
2014

17. Variable selection for a mixed population applied in proteomics

Author

J-F Giovannelli, Faouzi Adjed, Audrey Giremus, Pascal Szacherski, Noura Dridi, Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Laboratoire Electronique des Systèmes Santé (LE2S), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), CPU, and Giovannelli, Jean-François

Subjects
education.field_of_study, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Population, Posterior probability, Feature selection, Bayes factor, Regression analysis, Machine learning, computer.software_genre, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Bayesian multivariate linear regression, Artificial intelligence, education, business, Bayesian linear regression, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Statistical hypothesis testing, Mathematics
Abstract

The paper presents a variable selection method for biomarker discovery in proteomics. More specifically, it finds the most adequate variables among a given set in order to discriminate between two groups (healthy and pathological). This approach is developped within a Bayesian framework and relies on an optimal strategy that results in the choice of the most a posteriori probable model. The calculation of the posterior probabilities requiresmarginalization of unknown parameters. It is the main difficulty and a contribution of the paper is to provide a closed-form expression. The originality of the work is twofold: (1) we relax the standard hypothesis of linear regression models and (2) we present a multivariate test which directly accommodates possible correlations between the biomarkers. The effectiveness of the method is assessed through a simulated study and shows results in accordance with the theoritical optimality.

Published
2013

18. Blind Detection of Severely Blurred 1D Barcode

Author

Yves Delignon, Wadih Sawaya, Noura Dridi, François Septier, LAGIS-SI, Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), and Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Noise power, Channel (digital image), Computer science, Noise (signal processing), business.industry, Cyclostationary process, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Barcode, 01 natural sciences, law.invention, 010309 optics, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Artificial intelligence, Hidden Markov model, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Image restoration, Decoding methods, Communication channel
Abstract

International audience; In this paper, we present a joint blind channel estimation and symbol detection for decoding a blurred and noisy 1D barcode captured image. From an information transmission point of view, we show that the channel impulse response, the noise power and the symbols can be efficiently estimated by taking into account the signal structure such as the cyclostationary property of the hidden Markov process to estimate. Based on the Expectation-Maximisation method, we show that the new algorithm offers significative performance gain compared to classical ones pushing back the frontiers of the barcode technology.

Published
2010

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