Mignotte, Max - Département d'Informatique et recherche opérationnelle, Université de Montréal

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• INTRODUCTION AUX SYSTMES INFORMATIQUES
• A non-local regularization strategy for image deconvolution Max Mignotte *
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• TPs DE TRAITEMENT D'IMAGES CONSEILS SUR LA PROGRAMMATION ET LA
• AN UNSUPERVISED MULTISCALE APPROACH FOR THE DYNAMIC CONTOURBASED BOUNDARY DETECTION ISSUE IN ULTRASOUND IMAGERY
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• Pattern Recognition (2000) 1575}1584 Unsupervised segmentation using selforganizing map
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• BRAND X PICTURES [Miles N. Wernick, Yongyi Yang, Jovan G. Brankov,
• TRAVAIL PRATIQUE N o 8 DTECTION-SEGMENTATION DES CONTOURS D'UNE IMAGE
• Optical-flow based on an edge-avoidance procedure Pierre-Marc Jodoin a,*, Max Mignotte b
• INTRODUCTION INFORMATIQUES
• DMONSTRATION N o 2 Max Mignotte
• INTRODUCTION AUX SYSTMES INFORMATIQUES
• TRANSFORME Dpartement
• ESTIMATION OF MIXTURES OF PROBABILISTIC PCA WITH STOCHASTIC EM FOR THE 3D BIPLANAR RECONSTRUCTION OF SCOLIOTIC RIB CAGE
• NON-LOCAL IMAGE INTERPOLATION Hi^ep Quang Luong, Alessandro Ledda and Wilfried Philips
• A HIERARCHICAL STATISTICAL MODELING APPROACH FOR THE UNSUPERVISED 3D RECONSTRUCTION OF THE SCOLIOTIC SPINE
• Christine I. Podilchuk and Edward J. Delp
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• A de-texturing and spatially constrained K-means approach for image segmentation Max Mignotte
• AN ADAPTIVE SEGMENTATIONBASED REGULARIZATION TERM FOR IMAGE RESTORATION
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• Pattern Recognition 33 (2000) 1575}1584 Unsupervised segmentation using a self-organizing map
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• Quantitative Evaluation of a Novel Image Segmentation Algorithm Francisco J. Estrada and Allan D. Jepson
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• Statistical Sketching for Non-Photorealistic Rendering Models and Animations
• Universit e de Montr eal Analyse des champs de d eformation pour la
• INTRODUCTION AUX SYSTMES INFORMATIQUES
• INTRODUCTION ALGORITHMES
• POSTDOCTORAL REPORT BAYESIAN INFERENCE AND/OR OPTIMIZATION STRATEGIES
• EXAMEN INTRA Max Mignotte
• DMONSTRATION N o 3 Max Mignotte
• UNSUPERVISED HIERARCHICAL MARKOVIAN SEGMENTATION OF SONAR IMAGES
• TRAVAIL PRATIQUE N o 4 SIMULATION ET RECONSTRUCTION TOMOGRAPHIQUE
• STATISTIQUES DES NOTES IFT 2421 (H06) Max Mignotte
• EXAMEN INTRA Max Mignotte
• Universit de Montral Segmentation non-supervise d'images couleur par
• Unsupervised restoration of brain SPECT volumes Max Mignotte
• INTRODUCTION AUX SYSTMES INFORMATIQUES
• STATISTIQUES DES NOTES IFT 6150 (A00) Max Mignotte
• Unsupervised Detection and SemiAutomatic Extraction of Contours Using a Statistical Model and Dynamic Programming
• TRAVAIL PRATIQUE N o 1 TRANSFORME DE FOURIER
• DEVOIR N o 3 Max Mignotte & Said Benameur
• PROJET APPLIQU EN TRAITEMENT D'IMAGES Max Mignotte
• DMONSTRATION N o 4 Max Mignotte
• 4 Z
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• COMPRESSION Dpartement
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• FRQUENTIEL Dpartement
• INTRODUCTION INFORMATIQUES
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• Introduction correspondant
• INTRODUCTION AUX SYSTMES INFORMATIQUES DEVOIR N o 4
• INTRODUCTION AUX SYSTMES INFORMATIQUES PLAN DE COURS
• INTRODUCTION AUX SYSTMES INFORMATIQUES DEVOIR N o 3
• DEVOIR N o 4 Max Mignotte
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• UNSUPERVISED STATISTICAL SKETCHING FOR NON-PHOTOREALISTIC RENDERING MODELS
• STATISTIQUES DES NOTES IFT 2421 (H02) Max Mignotte
• UNSUPERVISED TEXTURE SEGMENTATION USING A STATISTICAL WAVELETBASED HIERARCHICAL MULTI DATA MODEL
• DMONSTRATION N o 1 Max Mignotte
• Universit e de Montr eal Ecole Polytechnique
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• DMONSTRATION N o 4 Max Mignotte
• Voici une liste de petites erreurs de typos qui se sont glisses dans vos notes de cours. Chapitre 1
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• INTRODUCTION INFORMATIQUES
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• Titre de la Th ese : Segmentation d'images SONAR par approche
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• UNSUPERVISED STATISTICAL SKETCHING FOR NONPHOTOREALISTIC RENDERING MODELS
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• IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 7, JULY 2006 1973 A Segmentation-Based Regularization
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• DMONSTRATION N o 12 Max Mignotte
• DEVOIR N o 2 Max Mignotte & Said Benameur
• BAYESIAN RENDERING WITH NONPARAMETRIC MULTISCALE PRIOR MODEL
• DMONSTRATION N o 8 Max Mignotte
• A Statistical Model for Contours in Images Francois Destrempes and Max Mignotte
• SPECT IMAGE RESTORATION VIA RECURSIVE INVERSE FILTERING CONSTRAINED BY A PROBABILISTIC MRI ATLAS
• A multiscale optimization approach for the dynamic contour-based boundary detection issue
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• Pattern Analysis Applications (2001)4:256--271 # 2001 SpringerVerlag London Limited
• DMONSTRATION N o 6 Max Mignotte
• School of Computer Science Computer Networks
• Abbreviated Journal Title ISSN Total CitesImpact factorImmediacy IndexArticlesCited Half-life AAPG BULL 0149-1423 4669 1,553 1 79 >10.0
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• INTRODUCTION ALGORITHMES
• 3D/2D registration and segmentation of scoliotic vertebrae using statistical models
• d'quations Introduction
• TRAITEMENT D'IMAGES OPRATIONS PONCTUELLES
• EXAMEN INTRA Max Mignotte
• An Improved Method for Cloud Removal in ASTER Data Change Detection
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• EXAMEN INTRA Max Mignotte
• STATISTIQUES DES NOTES IFT 6150 (A01) Max Mignotte
• TRAITEMENT D'IMAGES TRANSFORME DE FOURIER DISCRTE
• UNSUPERVISED MARKOVIAN SEGMENTATION OF SONAR IMAGES M. Mignotte z C.Collet z P.P'erez ffl P.Bouthemy ffl
• RECONNAISSANCE Dpartement
• GOMTRIQUE TOMOGRAPHIE
• TRAVAIL PRATIQUE N o 5 SEGMENTATION D'IMAGE TEXTURE
• UNSUPERVISED DETECTION OF CONTOURS USING A STATISTICAL MODEL F. Destrempes z M. Mignotte z
• PROOF COPY 013201JEI Comparison deconvolution techniques using
• ESTIMATION OF NOISE PARAMETERS ON SONAR IMAGES Francoise SCHMITT, Max MIGNOTTE,
• Dpartement d'Informatique
• EXAMEN INTRA Max Mignotte
• PRSENTATION Dpartement
• Unsupervised Localization of Shapes Using Statistical Models Franois Destrempes and Max Mignotte
• Computer Vision and Image Understanding (2000) doi:10.1006/cviu.2000.0844, available online http://www.idealibrary.com
• Multiresolution Markov Models for Signal and Image Processing
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• Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification
• Computer Vision and Image Understanding Vol. 76, No. 3, December, pp. 191204, 1999
• Pattern Analysis & Applications (2001)4:256271 2001 Springer-Verlag London Limited
• This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research
• Fusion of Regularization Terms For Image Restoration
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• Detection of diffuse abnormal perfusion in SPECT using a normal brain atlas
• A HIERARCHICAL GRAPH-BASED MARKOVIAN CLUSTERING APPROACH FOR THE UNSUPERVISED SEGMENTATION OF TEXTURED COLOR IMAGES
• LIGHT AND FAST STATISTICAL MOTION DETECTION METHOD BASED ON ERGODIC MODEL
• AN EDGE-PRESERVING ANATOMICAL-BASED REGULARIZATION TERM FOR THE NAS-RIF RESTORATION OF SPECT IMAGES
• An Adaptive Segmentation-based Regularization Term For Image Restoration
• UNSUPERVISED MOTION DETECTION USING A MARKOVIAN TEMPORAL MODEL WITH GLOBAL SPATIAL CONSTRAINTS
• UNSUPERVISED TEXTURE SEGMENTATION USING A STATISTICAL WAVELET-BASED HIERARCHICAL MULTI DATA MODEL
• Unsupervised statistical Sketching for Non-Photorealistic Rendering Models Max Mignotte
• Detection of Abnormal Diffuse Perfusion in SPECT Using a Normal Brain Atlas
• UNSUPERVISED DETECTION OF CONTOURS USING A STATISTICAL MODEL F. Destrempes
• A NEW AND SIMPLE SHAPE DESCRIPTOR BASED ON A NON-PARAMETRIC MULTISCALE MODEL
• Unsupervised Detection and Semi-Automatic Extraction of Contours Using a Statistical Model and Dynamic Programming
• 3D Biplanar Reconstruction of Scoliotic Vertebrae Using Statistical Models
• SEGMENTATION AND CLASSIFICATION OF BRAIN SPECT IMAGES USING 3D MARKOV RANDOM FIELD AND DENSITY MIXTURE ESTIMATIONS
• AN UNSUPERVISED MULTISCALE APPROACH FOR THE DYNAMIC CONTOUR-BASED BOUNDARY DETECTION ISSUE IN ULTRASOUND IMAGERY
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• Reconstruction 3D Biplanaire Dtaille des Vertbres Scoliotiques par Modlisation Statistique.
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• This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1
• Fusion of regularization terms for image restoration Max Mignotte
• Candidature au programme de Ph.D. DIRO (Dpartement d'informatique et de recherche oprationnelle)
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• This work is supported by NSFC project of China (No. 69931010). NATURAL COLOR IMAGE SEGMENTATION
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• FAST NON-LOCAL ALGORITHM FOR IMAGE DENOISING Jin Wang1,2
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• Super-Resolution Image Reconstruction
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• Unsupervised Statistical Method for Edgel Clustering with Applications to Shape Localization
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• A HIERARCHICAL STATISTICAL MODELING APPROACH FOR THE UNSUPERVISED 3D RECONSTRUCTION OF THE SCOLIOTIC SPINE
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• Unsupervised Localization of Shapes Using Statistical Models Franois Destrempes and Max Mignotte
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• d'quations Introduction
• DMONSTRATION N o 12 Max Mignotte
• INTRODUCTION AUX SYSTMES INFORMATIQUES
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• Unsupervised restoration of brain SPECT volumes Max Mignotte z Jean Meunier
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• Biplanar Reconstruction Scoliotic Vertebrae Using Statistical Models
• EXAMEN INTRA Max Mignotte
• TRAITEMENT D'IMAGES SEGMENTATION D'IMAGES
• EXAMEN INTRA Max Mignotte
• Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification
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• SEGMENTATION AND CLASSIFICATION OF BRAIN SPECT IMAGES USING 3D MARKOV RANDOM FIELD AND DENSITY MIXTURE ESTIMATIONS
• INTRODUCTION AUX SYSTMES INFORMATIQUES
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• [1] M. Mignotte. A de-texturing and spatially constrained k-means approach for image segmenta-tion. Pattern Recognition Letters, 32(2) :359 367, January 2011.
• STATISTIQUES DES NOTES IFT 2425 (H09) Max Mignotte
• DMONSTRATION N o 5 Max Mignotte
• DMONSTRATION N o 9 Max Mignotte
• DMONSTRATION N o 10 Max Mignotte
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• DEVOIR N o 2 Max Mignotte
• INTRODUCTION INFORMATIQUES
• STATISTICAL MODEL AND GENETIC OPTIMIZATION: APPLICATION TO PATTERN DETECTION IN SONAR IMAGES
• AN ADAPTIVE SEGMENTATION-BASED REGULARIZATION TERM FOR IMAGE RESTORATION
• IEEE SIGNAL PROCESSING MAGAZINE [90] NOVEMBER 2009 1053-5888/09/$26.002009IEEE Digital Object Identifier 10.1109/MSP.2009.934155
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• 3D/2D registration and segmentation of scoliotic vertebrae using statistical models
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• STATISTIQUES DES NOTES IFT 2425 (H08) Max Mignotte
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• INTRODUCTION AUX SYSTMES INFORMATIQUES
• A MULTISCALE OPTIMIZATION APPROACH FOR THE DYNAMIC CONTOURBASED BOUNDARY DETECTION ISSUE
• TRAVAIL PRATIQUE N o 2 DTECTION DES CONTOURS D'UNE IMAGE
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