MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation

This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic distribution of the neighborhood fields to diffuse the Gibbs potential space iteratively. By using the neighborhood distribution in the non-random and non-deterministic diffusion, both the estimation accuracy and the convergence speed are improved. In the paper, the hierarchical stochastic diffusion is also applied to the disparity field. The hierarchical approach reduces the memory and computational load, and increases the convergence speed of the potential space. The paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure. According to the experiments, the stochastic diffusion shows good estimation performance. The line field improves the estimation at the object boundary, and coincides with the object boundary with the useful contours. The stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and MRF models.     

Stochastic sampling algorithms for state estimation of jump Markovlinear systems

Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations. We present three globally convergent algorithms based on stochastic sampling methods for state estimation of jump Markov linear systems. The cost per iteration is linear in the data length. The first proposed algorithm is a data augmentation (DA) scheme that yields conditional mean state estimates. The second proposed scheme is a stochastic annealing (SA) version of DA that computes the joint MAP sequence estimate of the finite and continuous states. Finally, a Metropolis-Hastings DA scheme based on SA is designed to yield the MAP estimate of the finite-state Markov chain. Convergence results of the three above-mentioned stochastic algorithms are obtained. Computer simulations are carried out to evaluate the performances of the proposed algorithms. The problem of estimating a sparse signal developing from a neutron sensor based on a set of noisy data from a neutron sensor and the problem of narrow-band interference suppression in spread spectrum code-division multiple-access (CDMA) systems are considered     

Stereo matching using belief propagation

In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.     

模糊马尔可夫随机场理论在阴影检测中的应用

阴影的检测是目标检测、目标跟踪、视频监控等领域的一个关键问题。提出了一种基于模糊马尔可夫随机场的阴影检测算法。该算法把阴影检测问题看做是一个求最优化的像素点分类问题。对于输入的视频,提取背景图像,找出阴影和前景目标物体区域。通过计算阴影概率分布,前景概率分布,隶属度函数,建立模糊马尔可夫随机场。应用贝叶斯准则,最大后验(MAP)估计和条件迭代模式(ICM)算法,寻找最优化的模糊马尔可夫随机场,并利用最大隶属度原则消除模糊性,得到阴影检测的结果。实验证明,文中算法具有较好的阴影检测率和目标检测率。     

Multimodal estimation of discontinuous optical flow using Markovrandom fields

The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on Bayesian estimation associated with global statistical models, namely, Markov random fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported     

Tracking multiple visual targets via particle-based belief propagation

Multiple-target tracking in video (MTTV) presents a technical challenge in video surveillance applications. In this paper, we formulate the MTTV problem using dynamic Markov network (DMN) techniques. Our model consists of three coupled Markov random fields: 1) a field for the joint state of the multitarget; 2) a binary random process for the existence of each individual target; and 3) a binary random process for the occlusion of each dual adjacent target. To make the inference tractable, we introduce two robust functions that eliminate the two binary processes. We then propose a novel belief propagation (BP) algorithm called particle-based BP and embed it into a Markov chain Monte Carlo approach to obtain the maximum a posteriori estimation in the DMN. With a stratified sampler, we incorporate the information obtained from a learned bottom-up detector (e.g., support-vector-machine-based classifier) and the motion model of the target into the message propagation. Other low-level visual cues such as motion and shape can be easily incorporated into our framework to obtain better tracking results. We have performed extensive experimental verification, and the results suggest that our method is comparable to the state-of-art multitarget tracking methods in all the cases we tested.     

一种基于模糊Gibbs随机场的运动估计新算法

运动估计问题具有不适定性，单纯采用最大后验概率算法，实际上并未解决运动矢量的不连续、矢量的失真与随机噪声等棘手问题。本文应用模糊数据融合与Gibbs分布的基本思想，将运动场风险约束条件的概率分布模式有效地纳入阶段非凸函数(GNC)算法的局部迭代过程中，从而提高了运动估计效果。首先建立Gibbs的自适应能量模型，该模型可将基于特征和基于梯度的两类矢量按照优化约束条件进行融合；其次利用各种运动经验知识构造矢量的模糊风险决策表，该决策表可对Gibbs能量方程的每一步迭代收敛结果进行监督和修正，从而实现模糊数据融合。从收敛性和鲁棒性两方面说，模糊融合后的结果在原有基础上有明显提高。     

一种最大后验概率条件下的运动目标检测方法

提出一种最大后验概率条件下的运动目标检测方法.首先根据条件随机场模型和马尔可夫随机场模型建立了一个最大后验概率框架.在该框架内融入了连续标记场的时域信息、颜色信息和每个标记场的空域信息.考虑到传统方法融入的特征信息不够,提取目标的准确度不高,在目标模型中充分融入了颜色信息和边缘特征,以便获得更好的检测效果.实验结果表明提出的方法能正确检测到运动目标.     

基于Markov随机场模型的运动对象分割研究

本文探讨了一种基于高斯马尔可夫随机场(GMRF)模型的运动目标自动分割算法.该算法采用高斯混合分布描述视频序列的差分图像,对标准MAP算法进行了改进,使用快速方法计算后验边缘。首先对视频处理对象进行初始分割,获取初始运动数目以及相应的运动模型的初始参数,然后通过参数估计,不断更新模型参数,之后通过把每个运动区域和运动模型相关联,来同时估计多个运动区域,最终达到分割的目的.实验结果证明,本文所提的方法对运动目标分割具有较好的分割效果.     

Sequential stratified sampling belief propagation for multiple targets tracking

1 Introduction Multiple targets tracking (MTT) remains a challenging problem in computer vision. It has been applied widely in the intelligent surveillance, visual human-computer interface, and smart conference room among many others. MTT problem has been…     

基于最大后验概率的单视频时间超分辨率重建算法

任何视频摄像设备均具有一定的时间分辨率限制,时间分辨率不足会造成视频中存在运动模糊和运动混叠现象。针对这一问题常用的解决方法是空间去模糊和时间插值,然而这些方法无法从根本上解决问题。提出一种基于最大后验概率(MAP)的单视频时间超分辨率重建方法,该方法通过重建约束来确定条件概率模型,然后利用视频自身具有的时间自相似先验信息得到先验信息模型,最后求得基于最大后验概率的估计值,即通过对单个低时间分辨率视频重建来得到高时间分辨率视频,从而有效解决由于相机曝光时间过长所造成的“运动模糊”和相机帧率不足引起的“运动混叠”现象。通过理论分析与实验,证明了所提方法的有效性。     

Scene segmentation from visual motion using global optimization

This paper presents results from computer experiments with an algorithm to perform scene disposition and motion segmentation from visual motion or optic flow. The maximum a posteriori (MAP) criterion is used to formulate what the best segmentation or interpretation of the scene should be, where the scene is assumed to be made up of some fixed number of moving planar surface patches. The Bayesian approach requires, first, specification of prior expectations for the optic flow field, which here is modeled as spatial and temporal Markov random fields; and, secondly, a way of measuring how well the segmentation predicts the measured flow field. The Markov random fields incorporate the physical constraints that objects and their images are probably spatially continuous, and that their images are likely to move quite smoothly across the image plane. To compute the flow predicted by the segmentation, a recent method for reconstructing the motion and orientation of planar surface facets is used. The search for the globally optimal segmentation is performed using simulated annealing.     

基于MRF的复杂图像抠图

所谓复杂图像抠图就是从复杂图像中抠取出目标物体的一种图像处理算法。为了取得更好的抠图效果,提出了一种基于马尔可夫随机场的自然图像抠图方法。该方法首先手工把图像分成3个区域:前景区域、背景区域和未知区域;然后,再将未知区域用手工粗略地划分成几个相交的小区域;接着在每一个小区域内,以其中的未知区域的像素点为节点,定义抠图标号,同时在这些节点上面建立MRF抠图模型,并把这些标号赋给这些节点,这样抠图问题被定义为在这个MRF模型和它的Gibbs分布上MAP估计问题;继而再计算出每个小区域的掩像;最后把这些掩像合并,即得到输入图像最终的掩像。和其他算法相比,对复杂图像的抠图问题,该方法可以取得更好的抠图效果。     

Optimization by estimation of distribution with DEUM framework based on Markov random fields

This paper presents a Markov random field (MRF) approach to estimating and sampling the probability distribution in populations of solutions.The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields (DEUM).DEUM is a subclass of estimation of distribution algorithms (EDAs) where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph.The focus of this paper will be on describing the three main characteristics of DEUM framework,which distinguishes it from the traditional EDA.They are:1) use of MRF models,2) fitness modeling approach to estimating the parameter of the model and 3) Monte Carlo approach to sampling from the model.     

Moving object detection using Markov Random Field and Distributed Differential Evolution

In this article, we present an algorithm for detecting moving objects from a given video sequence. Here, spatial and temporal segmentations are combined together to detect moving objects. In spatial segmentation, a multi-layer compound Markov Random Field (MRF) is used which models spatial, temporal, and edge attributes of image frames of a given video. Segmentation is viewed as a pixel labeling problem and is solved using the maximum a posteriori (MAP) probability estimation principle; i.e., segmentation is done by searching a labeled configuration that maximizes this probability. We have proposed using a Differential Evolution (DE) algorithm with neighborhood-based mutation (termed as Distributed Differential Evolution (DDE) algorithm) for estimating the MAP of the MRF model. A window is considered over the entire image lattice for mutation of each target vector of the DDE; thereby enhancing the speed of convergence. In case of temporal segmentation, the Change Detection Mask (CDM) is obtained by thresholding the absolute differences of the two consecutive spatially segmented image frames. The intensity/color values of the original pixels of the considered current frame are superimposed in the changed regions of the modified CDM to extract the Video Object Planes (VOPs). To test the effectiveness of the proposed algorithm, five reference and one real life video sequences are considered. Results of the proposed method are compared with four state of the art techniques and provide better spatial segmentation and better identification of the location of moving objects.     

MRF–MAP–MFT visual object segmentation based on motion boundary field

In our earlier work, a two-pass motion estimation algorithm (TPA) was developed to estimate a motion field for two adjacent frames in an image sequence where contextual constraints are handled by several Markov random fields (MRFs) and the maximum a posteriori (MAP) configuration is taken to be the resulting motion field. In order to provide a trade-off between efficiency and effectiveness, the mean field theory (MFT) was selected to carry out the optimization process to locate the MAP with desirable performance. Given that currently in the disciplines of digital library [IEEE Trans. PAMI 18 (8) (1996); IEEE Trans. Image Process. 11 (8) (2002) 912] and video processing [IEEE Trans. Circ. Sys. Video Tech. 7 (1) (1997)] of utmost interest are the extraction and representation of visual objects, instead of estimating motion field, in this paper we focus on segmenting out visual objects based on spatial and temporal properties present in two contiguous frames in the same MRF–MAP–MFT framework. To achieve object segmentation, a “motion boundary field” is introduced which can turn off interactions between different object regions and in the mean time remove spurious object boundaries. Furthermore, in light of the generally smooth and slow velocities in-between two contiguous frames, we discover that in the process of calculating matching blocks, assigning different weights to different locations can result in better object segmentation. Experimental results conducted on both synthetic and real-world videos demonstrate encouraging performance.     

Image denoising based on hierarchical Markov random field

We propose a hierarchical Markov random field model-based method for image denoising in this paper. The method employs a Markov random field (MRF) model with three layers. The first layer represents the underlying texture regions. The second layer represents the noise free image. And the third layer is the observed noisy image. Iterated conditional modes (ICM) is used to find the maximum a posteriori (MAP) estimation of the noise free image and texture region field. The experimental results show that the new method can effectively suppress additive noise and restore image details.     

基于高斯马尔可夫随机场的MAP估计在高斯噪声滤波中的应用

提出了基于高斯马尔可夫随机场(GMRF)的最大后验概率(MAP)估计在图像高斯噪声滤波中的应用方法。根据高斯噪声的先验特点,建立基于高斯马尔可夫随机场的退化图像恢复模型,从而将图像高斯噪声滤波问题转化为求解最大后验概率问题。先验概率可以根据马尔可夫随机场(MRF)和吉布斯分布(GD)的等效性, 用GD的概率估计。为了求解最大后验概率,第一,通过期望最大化(EM）算法对GMRF模型进行参数估计。第二,用共轭梯度法将目标函数最小化。实验结果表明,与其他滤波器（如高斯滤波、维纳滤波等）相比,本文所阐述的方法在滤除高斯噪声、保持图像原有结构方面效果更好。     

On-line retrainable neural networks: improving the performance ofneural networks in image analysis problems

A novel approach is presented in this paper for improving the performance of neural-network classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level. The procedure includes: 1) a training algorithm for adapting the network weights to the current condition; 2) a maximum a posteriori (MAP) estimation procedure for optimally selecting the most representative data of the current environment as retraining data; and 3) a decision mechanism for determining when network retraining should be activated. The training algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization. The MAP estimation procedure models the network output as a Markov random field (MRF) and optimally selects the set of training inputs and corresponding desired outputs. Results are presented which illustrate the theoretical developments as well as the performance of the proposed approach in real-life experiments.     

A colorization algorithm based on local MAP estimation

This paper presents a colorization algorithm which adds color to monochrome images. In this paper, the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color image given a monochrome image. Markov random field (MRF) is used for modeling a color image which is utilized as a prior for the MAP estimation. The MAP estimation problem for a whole image is decomposed into local MAP estimation problems for each pixel. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color images with 25.7-32.6 dB PSNR values for eight images.