9 results for “markov random field”

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Research ArticleOpen accessIn the keywordsRange Image Segmentation by Randomized Region Growing and Bayesian Edge RegularizationSmaine Mazouzi, Mohamed BatoucheWe presented and evaluated a new Bayesian method for range image segmentation. The method proceeds in to stages. First, an initial segmentation was produced by a randomized region growing technique. The produced segmentation was considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, image pixels not labeled in the first stage … …Journal of Computer Science, Vol. 3, Issue 5 · 31 May 2007 · 6,924 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsInverse Correlations for Multiple Time Series and Gaussian Random Fields and Measures of Their Linear DeterminismR.J. Bhansali, L. IppolitiFor a discrete-time vector linear stationary process, {X(t)}, admitting forward and backward autoregressive representations, the variance matrix of an optimal linear interpolator of X(t), based on a knowledge of {X(t-j), j≠0}, is known to be given by Ri(0)-1 where Ri(0) denotes the inverse variance of the process. Let A=Is-1Ri(0)ˉ1R(0)ˉ1, where R(0) denotes the variance matrix of {X(t)} and Is an … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,424 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsA Flexible, Real-Time Algorithm for Simulating Correlated Random Fields and Its PropertiesMichael A. Kouritzin, Fraser Newton, Biao WuContemporary real-time problems like CAPTCHA generation and optical character recognition can be solved effectively using correlated random fields. These random fields should be produced on a graph in order that problems of any dimension and shape can be handled. However, traditional solutions are often too slow, inaccurate or both. Herein, the Quick Simulation Random Field algorithm to produce correlated random … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 29 Sep 2017 · 7,069 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsA New Modified Gaussian Mixture Model for Color-Texture SegmentationM. Sujaritha, S. AnnaduraiProblem statement: This study presents a new, simple and efficient modified Gaussian mixture model based clustering algorithm for color-texture segmentation. The proposed mixture model introduces a new component density function which incorporates spatial information and the weighting factor for neighborhood effect is fully adaptive to the image content. Approach: It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge-blurring … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,329 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsLinear Smoothing of Noisy Spatial Temporal SeriesValter Di Giacinto, Ian dryden, Luigi ippoliti, Luca RomagnoliThe main objective of the study is the development of a linear filter to extract the signal from a spatio-temporal series affected by measurement error. We assume that the evolution of the unobservable signal can be modelled by a space time autoregressive process. In its vectorial form, the model admits a state space representation allowing the direct application of the … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,311 accessesView PDF Cite

Research ArticleOpen accessIn the abstractA Deep Learning Framework for Classifying and Evaluating Yoga ExercisesMehar Latif Malik, Arun KhoslaYoga is a type of physical activity that combines physical postures with breathing techniques. Cardiovascular disease is the leading cause of illness and mortality. Yoga's potential involvement in cardiac recovery can be incorporated into cardiovascular rehabilitation. Classifying and evaluating yoga exercises using a deep learning framework plays a significant role in postoperative recovery. Home based rehabilitation often suffers from a … …Journal of Computer Science, Vol. 19, Issue 2 · 30 Jan 2023 · 6,091 accessesView PDF Cite

Research ArticleOpen accessIn the abstractUnsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of SuperpixelsRasha Shoitan, Mona M. MoussaSegmenting out the foreground from a single image remains a challenge in computer vision. Image co-segmentation has been used recently to alleviate the single image segmentation by exploiting the information of the common object to be segmented from a group of images having the same object. This research proposes an unsupervised co-segmentation technique based on saliency detection and optimized features … …Journal of Computer Science, Vol. 17, Issue 7 · 3 Aug 2021 · 7,003 accessesView PDF Cite

Research ArticleOpen accessIn the abstractCHARACTERIZATION OF MARKOV-BERNOULLI GEOMETRIC DISTRIBUTION RELATED TO RANDOM SUMSMohamed Gharib, Mahmoud M. Ramadan, Khaled A.H. Al-AjmiThe Markov-Bernoulli geometric distribution is obtained when a generalization, as a Markov process, of the independent Bernoulli sequence of random variables is introduced by considering the success probability changes with respect to the Markov chain. The resulting model is called the Markov- Bernoulli model and it has a wide variety of application fields. In this study, some characterizations are given … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 31 Mar 2014 · 6,706 accessesView PDF Cite

Research ArticleOpen accessIn the abstractApplication of MultiScale Hidden Markov Modeling Wavelet Coefficients to fMRI Activation DetectionFangyuan Nan, Yaonan Wang, Xiaoping MaProblem Statement: The problem of detection of functional magnetic resonance images (fMRIs), that is, to decide active and nonactive regions of human brain from fMRIs is studied in this paper. fMRI research is finding and will find more and more applications in diagnosing and treating brain diseases like depression and schizophrenia. At its initial stage fMRI detection are pixel-wise methods, … …Journal of Mathematics and Statistics, Vol. 4, Issue 4 · 31 Dec 2008 · 7,546 accessesView PDF Cite