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Image Dehazing by Incorporating markov random field with Dark Channel Prior

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Journal of Ocean University of China 2020年第3期19卷 551-560页

作者： XU Hao TAN Yibo WANG Wenzong WANG Guoyu College of Information Science and Engineering Ocean University of ChinaQingdao 266100China

As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.

关键词： image dehazing dark channel prior markov random field image segmentation

An Improved Non-Parametric Method for Multiple Moving Objects Detection in the markov random field

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Computer Modeling in Engineering & Sciences 2020年第7期124卷 129-149页

作者： Qin Wan Xiaolin Zhu Yueping Xiao Jine Yan Guoquan Chen Mingui Sun College of Electric and Information Engineering Hunan Institute of EngineeringXiangtan411104China National Engineering Research Laboratory for Robot Vision Perception and Control Hunan UniversityChangshaChina The Laboratory for Computational Neuroscience University of PittsburghPittsburghPA 15260USA

Detecting moving objects in the stationary background is an important problem in visual surveillance ***,the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic *** this paper,according to the basic steps of the background subtraction method,a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the markov random ***,the contributions are as follows:1)A new nonparametric strategy is utilized to model the background,based on an improved kernel density estimation;this approach uses an adaptive bandwidth,and the fused features combine the colours,gradients and positions.2)A markov random field method based on this adaptive background model via the constraint of the spatial context is proposed to extract objects.3)The posterior function is maximized efficiently by using an improved ant colony system *** experiments show that the proposed method demonstrates a better performance than many existing state-of-the-art methods.

关键词： Object detection non-parametric method markov random field

A New Encryption-Then-Compression Scheme on Gray Images Using the markov random field

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Computers, Materials & Continua 2018年第7期56卷 107-121页

作者： Chuntao Wang Yang Feng Tianzheng Li Hao Xie Goo-Rak Kwon South China Agricultural University Guangzhou 510642China Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen 518060China Chosun University Gwangju 501-759Republic of Korea

Compressing encrypted images remains a *** illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the *** this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus *** preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,***,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.

关键词： Encryption-then-compression compressing encrypted image markov random field compression efficiency factor graph.

A User Participation Behavior Prediction Model of Social Hotspots Based on Influence and markov random field

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China Communications 2017年第5期14卷 145-159页

作者： Yunpeng Xiao Jiawei Lai Yanbing Liu Chongqing Engineering Laboratory of Network and Information Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.

关键词： social network hotspot topic behavior prediction markov random field influence factor

Markerless human motion capture by markov random field and dynamic graph cuts with color constraints

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Science in China(Series F) 2009年第2期52卷 252-259页

作者： LI Jia WAN ChengKai ZHANG DianYong MIAO ZhenJiang YUAN BaoZong Institute of Information Science Beijing Jiaotong University Beijing 100044 China

Currently, many vision-based motion capture systems require passive markers attached to key loca- tions on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on markov random field （MRF） and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences.

关键词： human motion capture markov random field dynamic graph-cut color constrains

Recognition of 3-D objects based on markov random field models

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Frontiers of Electrical and Electronic Engineering in China 2006年第2期1卷 125-129页

作者： HUANG Ying DING Xiao-qing WANG Sheng-jin Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

The recognition of 3-D objects is quite a difficult task for computer vision *** paper presents a new object framework,which utilizes densely sampled grids with different resolutions to represent the local information of the input image.A markov random field model is then created to model the geometric distribution of the object key *** matching,which aims to find the accurate correspondence map between the key points of two images,is performed by combining the local similarities and the geometric relations together using the highest confidence first ***,a global similarity is calculated for object *** results on Coil-100 object database,which consists of 7200 images of 100 objects,are *** the numbers of templates vary from 4,8,18 to 36 for each object,and the remaining images compose the test sets,the object recognition rates are 95.75%,99.30%,100.0%and 100.0%,*** excellent recognition performance is much better than those of the other cited references,which indicates that our approach is well-suited for appearance-based object recognition.

关键词： Pattern recognition 3-D object recognition markov random field Highest confidence first

Prestack inversion based on anisotropic markov random field-maximum posterior probability inversion and its application to identify shale gas sweet spots

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Applied Geophysics 2015年第4期12卷 533-544,628页

作者：王康宁孙赞东董宁中国石油大学地质地球物理综合研究中心北京102249 中国石油化工股份有限公司石油勘探开发研究院北京100083 中国石油集团东方地球物理勘探有限责任公司河北涿州072751

Economic shale gas production requires hydraulic fracture stimulation to increase the formation permeability. Hydraulic fracturing strongly depends on geomechanical parameters such as Young＇s modulus and Poisson＇s ratio. Fracture-prone sweet spots can be predicted by prestack inversion, which is an ill-posed problem; thus, regularization is needed to obtain unique and stable solutions. To characterize gas-bearing shale sedimentary bodies, elastic parameter variations are regarded as an anisotropic markov random field. Bayesian statistics are adopted for transforming prestack inversion to the maximum posterior probability. Two energy functions for the lateral and vertical directions are used to describe the distribution, and the expectation-maximization algorithm is used to estimate the hyperparameters of the prior probability of elastic parameters. Finally, the inversion yields clear geological boundaries, high vertical resolution, and reasonable lateral continuity using the conjugate gradient method to minimize the objective function. Antinoise and imaging ability of the method were tested using synthetic and real data.

关键词： shale gas/oil sweet spot prestack inversion markov random field

Disease gene identification by using graph kernels and markov random fields

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Science China(Life Sciences) 2014年第11期57卷 1054-1063页

作者： CHEN BoLin LI Min WANG JianXin WU FangXiang Division of Biomedical Engineering University of Saskatchewan School of Information Science and Engineering Central South University Department of Mechanical Engineering University of Saskatchewan

Genes associated with similar diseases are often functionally *** principle is largely supported by many biological data sources,such as disease phenotype similarities,protein complexes,protein-protein interactions,pathways and gene expression *** multiple types of biological data is an effective method to identify disease genes for many genetic *** capture the gene-disease associations based on biological networks,a kernel-based markov random field(MRF)method is proposed by combining graph kernels and the MRF *** the proposed method,three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks,respectively,and a novel weighted MRF method is developed to integrate those *** addition,an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF *** experiments are carried out by integrating known gene-disease associations,protein complexes,protein-protein interactions,pathways and gene expression *** proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm,achieving an AUC score of 0.771 when integrating all those biological data in our experiments,which indicates that our proposed method is very promising compared with many existing methods.

关键词： disease gene identification data integration markov random field graph kernel Bayesian analysis

Segmentation of MS lesions using entropy-based EM algorithm and markov random fields

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Journal of Biomedical Science and Engineering 2011年第8期4卷 552-561页

作者： Ahmad Bijar Mahdi Mohamad Khanloo Antonio Penalver Benavent Rasoul Khayati Department of Biomedical Engineering Shahed UniversityTehranIran Department de Estadística Matemáticas e InformáticaUniversidad Miguel HernándezElcheSpain

This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with three kernels as cerebrospinal fluid (CSF), normal tissue and Multiple Sclerosis lesions. To estimate this model, an automatic Entropy based EM algorithm is used to find the best estimated Model. Then, markov random field (MRF) model and EM algorithm are utilized to obtain and upgrade the class conditional probability density function and the apriori probability of each class. After estimation of Model parameters and apriori probability, brain tissues are classified using bayesian classification. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.

关键词： Gaussian Mixture Model EM Entropy markov random field Multiple Sclerosis