基于分层置信度传播的光流估计方法

置信度传播算法作为一种有效的寻找图像间对应点的方法,近年来被广泛应用于光流估计.但是在估计大位移高精度光流时,将置信度传播直接应用于原图像会导致标签空间过大和处理时间过长的问题.为了克服这个缺点,我们提出了一种基于分层置信度传播的算法来估计高精度大位移光流.本文方法将输入图像视作马尔科夫随机场,为了提高效率,在超像素和像素两个层面上执行置信度传播.我们将超像素层得到的基础位移结果作为粗略的位移参考值,可以有效地减小像素层置信度传播的标签空间,并在有限的标签空间内得到高精度的光流估计结果.MPI Sintel光流数据集上的实验结果显示本文提出的方法在精度和速度上都取得了较好的结果.     

Unsupervised image segmentation based on MRFs and graph cuts

Markov random fields （MRFs） can be used for a wide variety of vision problems. In this paper we will propose a Markov random field （MRF） image segmentation model. The theoretical framework is based on Bayesian estimation via the energy optimization. Graph cuts have emerged as a powerful optimization technique for minimizing energy functions that arise in low-level vision problem. The theorem of Ford and Fulkerson states that min-cut and max-flow problems are equivalent. So, the minimum s/t cut problem can be solved by finding a maximum flow from the source s to the sink t. we adopt a new min-cut/max-flow algorithm which belongs to the group of algorithms based on augmenting paths. We propose a parameter estimation method using expectation maximization （EM） algorithm. We also choose Gaussian mixture model as our image model and model the density associated with one of image segments （or classes） as a multivariate Gaussian distribution. Characteristic features related to the information in color, texture and position are extracted for each pixel. Experimental results will be provided to illustrate the performance of our method.     

A spatially constrained generative asymmetric Gaussian mixture model for image segmentation

Accurate image segmentation is an essential step in image processing, where Gaussian mixture models with spatial constraint play an important role. Nevertheless, most methods suffer from one or more challenges such as limited robustness to noise, over-smoothness for segmentations, and lack of flexibility to fit the observed data. To address these issues, in this paper, we propose a generative asymmetric Gaussian mixture model with spatial constraint for image segmentation. The asymmetric distribution is modified to be easily incorporated the spatial information. Then our asymmetric model can be constructed based on the posterior and prior probabilities of within-cluster and between-cluster. Based on the Kullback-Leibler divergence, we introduce two pseudo-likelihood quantities which consider the neighboring priors of within-cluster and between-cluster. Finally, we derive an expectation maximization algorithm to maximize the approximation of the data log-likelihood. We compare our algorithm with state-of-the-art segmentation approaches to demonstrate the superior performance of the proposed algorithm.     

Markov Random Field modeling,inference & learning in computer vision & image understanding: A survey

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.     

MRFs基因家族对肉品质的影响

肌肉调节因子(MRFs)基因家族编码4种肌肉特异性转录因子,分别是MyoD、MyoG、Myf5和Myf6,它们是控制骨骼肌生成的关键调节因子,共同控制肌肉的生成。研究表明,这些肌肉特异性转录因子与肌肉生长和肉质有着密切的关系。本文就生肌调节因子MRFs家族的结构特性、作用机理及各成员对肉质的影响进行了综述,并对其研究进展及应用前景进行了讨论。      

Interactive color image segmentation via iterative evidential labeling

We develop an interactive color image segmentation method in this paper. This method makes use of the conception of Markov random fields (MRFs) and D–S evidence theory to obtain segmentation results by considering both likelihood information and priori information under Bayesian framework. The method first uses expectation maximization (EM) algorithm to estimate the parameter of the user input regions, and the Bayesian information criterion (BIC) is used for model selection. Then the beliefs of each pixel are assigned by a predefined scheme. The result is obtained by iteratively fusion of the pixel likelihood information and the pixel contextual information until convergence. The method is initially designed for two-label segmentation, however it can be easily generalized to multi-label segmentation. Experimental results show that the proposed method is comparable to other prevalent interactive image segmentation algorithms in most cases of two-label segmentation task, both qualitatively and quantitatively.