993 research outputs found

    Adaptive segmentation of textured images by using the coupled Markov random field model

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    Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation

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    Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov random field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation--Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Change detection in optical aerial images by a multilayer conditional mixed Markov model

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    In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth

    Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

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    This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

    Texture Segmentation Based on Wavelet and Kohonen Network for Remotely Sensed Images

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    In this paper, an approach based on wavelet decomposition and Kohonen's self-organizing map is developed for image segmentation. After performing the 2D wavelet transform of image, some features are extracted for texture segmentation, and the Kohonen neural network is used to accomplish feature clustering. The experimental results demonstrated the satisfactory effect of the proposed approach both for simulated textured image and multi-spectral remotely sensed image

    On a parameter estimation method for Gibbs-Markov random fields

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    Cataloged from PDF version of article.This correspondence is about a Gibbs-Markov random field (GMRF) parameter estimation technique proposed by Derin and Elliott. We will refer to this technique as the histogramming (H) method. First, the relation of the H method to the (conditional) maximum likelihood (ML) method is considered. Second, a bias-reduction based modification of the H method is proposed to improve its performance, especially in the case of small amounts of image data
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