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International Conference on Systems, Control and Information Technologies (SCIT)

作者： Vozhov, Stanislav S. Chimitova, Ekaterina V. Novosibirsk State Tech Univ Novosibirsk Russia

ISBN: (纸本)9783319489230;9783319489223

In many practical situations, we only know the upper bound triangle of the measurement error. It means that the precise measurement is located on the interval (x - triangle, x + triangle). In other words, the data can be represented as a sample of interval observations. When performing statistical tests, ignoring this uncertainty in data may lead to unreliable decisions. For interval data, standard nonparametric and semiparametric methodologies include various modifications of the logrank test for comparing distribution functions. The statistics of the logrank homogeneity tests are based on comparing the nonparametric maximum likelihood estimates (NPMLE) of the distribution functions. In this paper, NPMLE is calculated by the icm-algorithm (iterative convex minorant algorithm). The purpose of this paper is to investigate some homogeneity tests for interval data and to carry out the comparative analysis in terms of the power of tests for close competing hypotheses.

关键词： Interval data Generalized logrank test icm algorithm

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SCALABLE REGION-BASED IMAGE RETRIEVAL SYSTEM IN THE WAVELET TRANSFORM DOMAIN

SCALABLE REGION-BASED IMAGE RETRIEVAL SYSTEM IN THE WAVELET ...

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Sakji-Nsibi, Sarra Benazza-Benyahia, Amel Univ Carthage Higher Sch Commun Tunis SUPCom Res Lab COSIM Tunis Tunisia

ISBN: (纸本)9781509036110

In this paper, we have designed a new method for region based retrieval of textured monochannel images. To perform segmentation and region feature extraction, the concept of Markov Random Fields (MRF) in the Wavelet Transform (WT) domain is considered and the Iterated Conditional Modes (icm) algorithm is employed. The novelty of our approach consists in exploiting the hierarchical organization of features through scales and iterations in order to design a multiresolution and iteratively refined search procedure until user satisfaction. Experimental results show that our method achieves a suitable tradeoff between complexity and reliability of the search procedure.

关键词： RBIR system Wavelet Transform Joint Bayesian segmentation and feature extraction Markov random fields icm algorithm Scalable search

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Image Registration using Bayes Theory and a Maximum Likelihood Framework with an EM algorithm

Image Registration using Bayes Theory and a Maximum Likeliho...

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1st International Workshop on Image Processing Theory

作者： Park, Jonghyun Cho, Wanhyun Kim, Sunworl Park, Soonyoung Lee, Myungeun Jeong, Changbu Lim, Junsik Lee, Gueesang Chonnam Natl Univ Sch Elect & Comp Engn Kwangju South Korea Chonnam Natl Univ Dept Stat Kwangju South Korea Mokpo Natl Univ Dept Elect Engn Mokpo South Korea Honam Univ Dept Internet Software Kwangju South Korea

ISBN: (纸本)9781424433216

A novel image registration algorithm that uses two kinds of information is presented: One kind is the shape information of an object and the other kind is the intensity information of a voxel and its neighborhoods consisting of the object. We, first, segment the medical volume data using the Markov random field model and the icm algorithm and extract the surface region of the object from a segmented volume data. Second, we use the hidden labeling variables and likelihood method to statistically model the intensity distribution of each voxel at the surface region. We adopt the Bernoulli probability model to formulate a prior distribution of the labeling variable for the transformed voxels. The Gaussian mixture model is taken as a probability distribution function for the intensity of the transformed voxel. We use the EM algorithm to get the proper estimators for the parameters of the complete-data log likelihood function. The EM algorithm consists of two steps: the E-step and M-step. In the E-step, we compute the posterior distribution of the labeling variable by taking the expectation for the log-likelihood function. Next, we drive the estimators for all of the parameters by maximizing this function iteratively in the M-step. Then, we define a new registration measure with the Q-function obtained by the EM algorithm. We evaluate the precision of the proposed approach by comparing the registration traces of the Q-function obtained from the original image and its transformed image with respect to x-translation and rotation. The experimental results show that our method has great potential power to register various medical images given by different modalities.

关键词： Medical image segmentation and registration Markov random field icm algorithm Maximum likelihood framework Gaussian mixture model EM algorithm

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EM procedures using mean field-like approximations for Markov model-based image segmentation

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PATTERN RECOGNITION 2003年第1期36卷 131-144页

作者： Celeux, G Forbes, F Peyrard, N Inria Rhone Alpes Zirst F-38334 Saint Ismier France

Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： image segmentation hidden Markov random fields EM algorithm icm algorithm pseudo-likelihood mean field approximation simulated field

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Approximate Bayes factors for image segmentation: The Pseudolikelihood Information Criterion (PLIC)

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2002年第11期24卷 1517-1520页

作者： Stanford, DC Raftery, AE Insightful Corp Seattle WA 98109 USA Univ Washington Dept Stat Seattle WA 98195 USA

We propose a method for choosing the number of colors or true gray levels in an image;this allows fully automatic segmentation of images. Our underlying probability model is a hidden Markov random field. Each number of colors considered is viewed as corresponding to a statistical model for the image, and the resulting models are compared via approximate Bayes factors. The Bayes factors are approximated using BIC (Bayesian Information Criterion), where the required maximized likelihood is approximated by the Qian-Titterington pseudolikelihood. We call the resulting criterion PLIC (Pseudolikelihood Information Criterion). We also discuss a simpler approximation, MIMIC (Marginal Mixture Information Criterion), which is based only on the marginal distribution of pixel values. This turns out to be useful for initialization and it also has moderately good performance by itself when the amount of spatial dependence in an image is low. We apply PLIC and MMIC to a medical image segmentation problem.

关键词： BIC color image quantization icm algorithm image segmentation Markov random field medical image mixture model posterior model probability pseudolikelihood satellite image

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Multisource classification using icm and Dempster-Shafer theory

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2002年第2期51卷 277-281页

作者： Foucher, S Germain, M Boucher, JM Bénié, GB Univ Sherbrooke Ctr Applicat & Rech Teledetect Sherbrooke PQ J1K 2R1 Canada Ecole Natl Super Telecommun Bretagne Brest France

We propose to use evidential reasoning in order to relax Bayesian decisions given by a Markovian classification algorithm (icm). The Dempster-Shafer rule of combination enables us to fuse decisions in a local spatial neighborhood which we further extend to be multisource. This approach enables us to more directly fuse information. Application to the classification of very noisy images produces interesting results.

关键词： data fusion Dempster-Shafer theory icm algorithm multisource classification remote sensing

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Computation of the NPMLE of distribution functions for interval censored and truncated data with applications to the Cox model

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 1998年第1期28卷 33-50页

作者： Pan, W Chappell, R Univ Minnesota Div Biostat Minneapolis MN 55455 USA Univ Wisconsin Dept Stat Madison WI 53706 USA Univ Wisconsin Dept Biostat Madison WI 53706 USA

The iterative convex minorant (icm) algorithm (Groeneboom and Wellner, 1992) is widely believed to be much faster than the EM algorithm (Turnbull, 1976) in computing the NPMLE of the distribution function for interval censored data. Our formulation of the icm helps to explore its connection with the gradient projection (GP) method that is commonly used in the constrained optimization area. Difficulties in extending the icm to left truncated and interval censored data are also explained. Simulations were conducted to assess the performance of these methods. In particular, the GP is shown to be much faster than the EM. Due to its generality and simplicity the GP method is easily applied to the Cox proportional hazards model with left truncated and interval censored data. The methodology is illustrated by using the Massachusetts Health Care Panel Study dataset. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： EM algorithm gradient projection icm algorithm isotonic regression proportional hazards model

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BAYESIAN FILTERING AND SUPERVISED CLASSIFICATION IN IMAGE REMOTE-SENSING

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 1995年第2期20卷 203-225页

作者： GRANVILLE, V RASSON, JP FAC UNIV NOTRE DAME PAIX DEPT MATHB-5000 NAMURBELGIUM

In the framework of image remote sensing, Markov random fields are used to model the distribution of points both in the 2-dimensional geometrical layout of the image and in the spectral grid. The problems of image filtering and supervised classification are investigated. The mixture model of noise developed here and appropriate Gibbs densities yield a same approach and a same efficient icm algorithm both for filtering and classifying.

关键词： MARKOV RANDOM FIELDS FILTERING IMAGE REMOTE SENSING SUPERVISED CLASSIFICATION icm algorithm

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A MRF-BASED PARALLEL-PROCESSING FOR SPEECH RECOGNITION USING LINEAR PREDICTIVE HMM

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1994年第10期E77D卷 1142-1147页

作者： NODA, H SHIRAZI, MN NAKATSUI, M Japan Ministry of Posts and Telecommunications Kobe-shi Japan

Parallel processing in speech recognition is described, which is carried out at each frame on time axis. We have already proposed a parallel processing algorithm for HMM (Hidden Markov Model)-based speech recognition using Markov Random Fields (MRF) [1]. The parallel processing is realized by modeling the hidden state sequence by an MRF and using the Iterated Conditional Modes (icm) algorithm [2] to estimate the optimal state sequence given an observation sequence and model parameters. However this parallel processing with the icm algorithm is applicable only to the standard HMM but not to the improved HMM like the linear predictive HMM which takes into account the correlations between nearby observation vectors. In this paper we propose a parallel processing algorithm applicable to the correlation-considered HMM, where a new deterministic relaxation algorithm [8] called the Generalized icm (Gicm) algorithm is used instead of the icm algorithm for estimation of the optimal state sequence. Speaker independent isolated word recognition experiments show the effectiveness of the proposed parallel processing using the Gicm algorithm.

关键词： PARALLEL PROCESSING SPEECH RECOGNITION MRF MODEL HMM icm algorithm

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Informative priors for the Bayesian classification of satellite images

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Journal of the American Statistical Association 1994年第426期89卷 703-703页

作者： Frigessi, Arnoldo Stander, Julian Associate Professor Laboratorio di Statistica Università di Venezia Italy School of Mathematics and Statistics University of Plymouth UK

In the Bayesian classification of satellite images, a prior distribution is used that aims to model the belief of spatial homogeneity of the underlying region We extend this prior distribution to model certain topographical features of the area such as the position of the roads, the slopes, and the aspects We demonstrate the effectiveness of this prior distribution in a reconstruction algorithm by means of a simulation study in which the quality of the result is assessed by a comparison of estimated and known covertypes We apply the algorithm to real data with success [ABSTRACT FROM AUTHOR]

关键词： Geographical Information System icm algorithm Image reconstruction Markov random fields Quality assessment Remote sensing

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