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Unsupervised parallel image classification using Markovian models

Zoltan Kato, Josiane Zerubia, Marc Berthod
1999 Pattern Recognition  
This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF).  ...  One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models.  ...  We have presented some iterative unsupervised parallel segmentation algorithms for both monogrid and hierarchical Markovian models.  ... 
doi:10.1016/s0031-3203(98)00104-6 fatcat:g2v3xzokkbamxozjp2bkmqbgje

Unsupervised parallel image classification using a hierarchical Markovian model

Z. Kato, J. Zerubia, M. Berthod
Proceedings of IEEE International Conference on Computer Vision  
doi:10.1109/iccv.1995.466790 dblp:conf/iccv/KateZB95 fatcat:vyexiq37rbeexeburgs4jvxtdq

Markov Random Field and Fuzzy Logic Modeling in Sonar Imagery: Application to the Classification of Underwater Floor

M. Mignotte, C. Collet, P. Pérez, P. Bouthemy
2000 Computer Vision and Image Understanding  
This preliminary classification is finally refined thanks to a Markov random field model which allows to incorporate spatial homogeneity properties one would expect for the final classification map.  ...  This § paper proposes an original method for the classification of seafloors from high resolution sidescan sonar images.  ...  It is based on a windowwise classification of cast shadows, which are extracted beforehand, using a combination of fuzzy logic and Markovian modeling.  ... 
doi:10.1006/cviu.2000.0844 fatcat:46riooisgneatj4j6o6lzdj2na

Unsupervised Markovian Segmentation on Graphics Hardware [chapter]

Pierre-Marc Jodoin, Jean-François St-Amour, Max Mignotte
2005 Lecture Notes in Computer Science  
This paper explains how classical iterative site-wise-update algorithms commonly used to optimize global Markovian cost functions can be efficiently implemented in parallel by fragment shaders driven by  ...  This contribution shows how unsupervised Markovian segmentation techniques can be accelerated when implemented on graphics hardware equipped with a Graphics Processing Unit (GPU).  ...  Among the image-space based techniques are the Markovian algorithms [2, 3] which incorporate both image and spatial characteristics by using Markov Random Fields (MRF) as a priori models.  ... 
doi:10.1007/11552499_50 fatcat:kvqzwvmxcjalrnxxr6avbzaagq

Extension of higher-order HMC modeling with application to image segmentation

Lamia Benyoussef, Cyril Carincotte, Stéphane Derrode
2008 Digital signal processing (Print)  
Model parameters estimation is performed from an extension of the general Iterative Conditional Estimation (ICE) method to take into account memories, which makes the classification algorithm unsupervised  ...  The higher-order HMC model is then evaluated in the image segmentation context. A comparative study conducted on a simulated image is carried out according to the order of the chain.  ...  In case of unsupervised classification, Markovian parameters have to be estimated from the observed data only.  ... 
doi:10.1016/j.dsp.2007.10.010 fatcat:r4v7bgih3vhmtecduprc2nkyku

Image Denoising by Averaging of Piecewise Constant Simulations of Image Partitions

Max Mignotte
2007 IEEE Transactions on Image Processing  
This sequential averaging allows to obtain, under our image model, an approximation of the image to be recovered in the minimal mean square sense error.  ...  Index Terms-Image denoising, Markov chain Monte-Carlo (MCMC) simulations, Markovian segmentation.  ...  Unsupervised Markovian Oversegmentation To this end, we have considered the monoscale version of the Markovian estimation model of segmentation into classes described in [35] , and already successfully  ... 
doi:10.1109/tip.2006.887729 pmid:17269644 fatcat:mag5jxypv5ccdgga32q65jj5ry

Table of Contents

2022 IEEE Transactions on Cybernetics  
Yang 5464 Class Incremental Learning With Few-Shots Based on Linear Programming for Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Kulik 5148 Deep Generative Model Using Unregularized Score for Anomaly Detection With Heterogeneous Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcyb.2022.3179920 fatcat:vu3og2ajezgqzdq3hkw3wg3u3q

Unsupervised Bayesian wavelet domain segmentation using Potts-Markov random field modeling

Ali Mohammad-Djafari
2005 Journal of Electronic Imaging (JEI)  
This model performs a fully unsupervised segmentation, on images composed of homogeneous regions, by introducing a hidden Markov Model (HMM) for the regions to be classified and Gaussian distributions  ...  This paper describes a new fully unsupervised image segmentation method based on a Bayesian approach and a Potts-Markov Random Field (PMRF) model that are performed in the wavelet domain.  ...  A third type is finally the one to which our method pertains and englobes the Markovian methods, supervised and unsupervised.  ... 
doi:10.1117/1.2139967 fatcat:27ow4ynw2fcf7ku22ss7qgzxua

Clustering of polarization-encoded images

Jihad Zallat, Christophe Collet, Yoshitate Takakura
2004 Applied Optics  
Two methods of analysis are proposed: polarization contrast enhancement and a more-sophisticated image-processing algorithm based on a Markovian model.  ...  Polarization-encoded imaging consists of the distributed measurements of polarization parameters for each pixel of an image. We address clustering of multidimensional polarization-encoded images.  ...  In this paper we address the analysis of polarization-encoded images and explore the potential for use of this information in classification systems.  ... 
doi:10.1364/ao.43.000283 pmid:14735948 fatcat:r2uehhsnubeijbbm25srjnbzre

Front Matter: Volume 9813

2015 MIPPR 2015: Pattern Recognition and Computer Vision  
using a Base 36 numbering system employing both numerals and letters.  ... Paper Numbering: Proceedings of SPIE follow an e-First publication model. A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  algorithm and its implementation [9813-23] 9813 0F Drug-related webpages classification using images and text information based on multi- kernel learning [9813-35] 9813 0G Fusion of infrared and  ... 
doi:10.1117/12.2230498 fatcat:qvz7wcmalfhc3c77sybxtc3fji

Motion compensated color video classification using Markov Random Fields [chapter]

Zoltan Kato, Ting-Chuen Pong, John Chung-Mong Lee
1997 Lecture Notes in Computer Science  
This paper deals with the classification of color video sequences using Markov Random Fields (MRF) taking into account motion information.  ...  Parameter estimation is also considered in the paper and results are shown on color video sequences using both the simple and motion compensated models.  ...  The classification model is defined in a Markovian framework and uses a first order potential derived from a three-variate Gaussian distribution in order to tie the final classification to the observed  ... 
doi:10.1007/3-540-63930-6_189 fatcat:nzn34udy55cczbpq2uw75mifge

Estimating marbling score in live cattle from ultrasound images using pattern recognition and neural network procedures1

John R. Brethour
1994 Journal of Animal Science  
This was more accurate ( P < .001) than using the same features in a multiple regression model. Images were used from 53 cattle in the training set and from 108 cattle in the validation set.  ...  When the results were subjected to receiver operating characteristic analysis, accuracies in grade classification were comparable to clinical, diagnostic imaging evaluations.  ...  The high A, values confirmed that any of the three methods might be used for transforming image texture statistics into carcass grade classifications.  ... 
doi:10.2527/1994.7261425x pmid:8071165 fatcat:ibcycebf6zezzoktkpcihrt4ji

A Review on Machine Learning Algorithms

Anushree Raj
2019 International Journal for Research in Applied Science and Engineering Technology  
Machine learning algorithms consist of identifying and validating models to optimize a performance criterion using historical, present, and future data.  ...  The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically.  ...  Learning (training): Learn a model using the training data. Testing: Test the model using unrevealed test data to appraise the model accuracy by itself, continually using trial and error.  ... 
doi:10.22214/ijraset.2019.6138 fatcat:7flbjojwxvevph6xdvhi37gmzq

Segmentation and Real Time Control

Pierre Bonton, Mustapha Derras, Christophe Debain, Michel Berducat
1996 Microscopy Microanalysis Microstructures  
Abstract. 2014 An original region segmentation based on a Markovian modelling of a set of sites (representing a 16 x 16 pixels elementary region) is presented.  ...  In order to obtain a real time application (200 ms computing time per image), a simple parallelization of the algorithm and a control unit (control servoing) are realized.  ...  -The implements of the method of image segmentation by using the Markovian modelling, have been inspired by the deterministic relaxation algorithm I.C.M. (Iterated Conditional Mode) [8].  ... 
doi:10.1051/mmm:1996132 fatcat:7xrljcpld5dfhim3d7mmc5ajhq

Hybrid genetic optimization and statistical model based approach for the classification of shadow shapes in sonar imagery

M. Mignotte, C. Collet, P. Perez, P. Bouthemy
2000 IEEE Transactions on Pattern Analysis and Machine Intelligence  
AbstractÐWe present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar.  ...  Then, the classification problem is defined as a two-step process. First, the detection problem of a region of interest in the input image is stated as the minimization of a cost function.  ...  In our approach, we use the result of an unsupervised two-class Markovian segmentation of the input sonar image [5] .  ... 
doi:10.1109/34.825752 fatcat:irttqoqipnbvridkcbo55mxmdu
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