一种自适应调整K-r的混合高斯背景建模和目标检测算法

针对非平稳背景下的复杂场景,该文提出一种自适应调整K-r的混合高斯背景建模和目标检测算法。该方法利用混合高斯模型(GMM)学习每个像素在时间域上的分布,构建自适应调整高斯分量K的方法,并针对不同情况,对描述像素的高斯分量数进行增加、删除或合并;在此基础上,模型参数更新式中引入了两个新的参数,能够根据实际情况自适应调整r值,使得背景建模和目标检测能够准确实时地随像素变化而变化,从而减少了运动目标信息的损失,提高了算法的鲁棒性和收敛性。实验表明,该算法在有诸多不确定因素的序列视频中能够迅速响应实际场景的变化,实现自适应背景建模和准确的目标检测。     

基于时空的混合高斯背景建模的运动目标检测

本文针对摄像机固定下的复杂背景环境,提出一种基于时空的自适应混合高斯背景建模方法,克服经典混合高斯模型（Gaussian Mixture Model,GMM）中只考虑单个像素的独立性而忽略相邻像素间的空间域相关性。首先采用混合高斯模型对每个像素在时间域上进行学习,然后利用相邻像素的自信息对背景及前景目标进行二次聚类,以修正错误的判断。实验结果表明,与经典混合高斯背景算法相比,本文提出的方法目标检测结果更加完整,具有更强的鲁棒性和很好的应用前景。     

基于混合Gamma建模与MRF的SAR图像分割方法

文中基于结合利用SAR图像统计信息和像素的空间约束信息的思路,提出了基于混合Gamma建模和MRF的分割方法,算法中利用混合Gamma模型对SAR图像进行统计建模,利用MRF模型对像素空间相关性进行建模。文中验证了混合Gamma模型参数估计的准确性以及自适应估计分割类别数目的有效性。最后用模拟SAR数据和实测SAR数据验证了文中所提出的分割算法的有效性。     

高斯混合分布激光中心线提取方法

针对激光辅助立体视觉测量中的非高斯非对称分布激光条的匹配中心线提取精度较低的问题,提出一种基于高斯混合模型的激光中心线提取方法。首先分析了激光散斑对图像的影响,选择均值滤波方法有效去除激光散斑噪声;然后利用最大类间方差(OTSU)阈值分割方法对光条位置进行粗定位;最后,利用本文提出的高斯混合模型提取激光条亚像素中心线,该模型可准确地描述激光条横截面光强分布特性,从而能够实现光条中心极值点的高精度提取。实验结果表明该方法能够高效稳定的提取激光条中心线。     

基于NSST和高斯混合模型的医学图像融合算法

提出了一种基于非下采样剪切波变换（Non-subsampled shearlet transform, NSST）和高斯混合模型的医学彩色图像融合算法。首先将彩色图像转换到HSI颜色空间,提取其色度分量图像、饱和度分量图像和强度分量图像;然后对强度分量图像和MRI图像进行NSST变换,其中低频系数采用基于区域系数改进拉普拉斯能量和（Sum-modified-Laplacian,SML）加权的融合规则,高频系数采用高斯混合模型估计参数取大的融合规则;对融合后的系数进行NSST逆变换重构融合后的强度分量图像;最后将融合后的强度分量图像与色度分量图像、饱和度分量图像混合得到融合后的HSI图像,再将HSI图像转换到RGB颜色空间得到融合后的彩色图像。仿真实验表明,该算法在视觉效果和客观评价指标上具有更好的融合效果。     

自适应空间约束融入混合模型的遥感图像分割

为了降低图像噪声的影响并提高遥感图像分割精度,提出了一种自适应空间约束融入混合模型的遥感图像分割算法。考虑到学生t分布具有重尾特性比高斯分布更具有鲁棒性,利用学生t混合模型(Student’s-t Mixture Model, SMM)建模像素光谱测度概率分布。为了避免图像噪声对分割结果的影响,基于马尔可夫随机场利用局部像素类属概率定义组份权重,将像素空间相关性融入SMM,进而构建出空间约束图像分割模型。为了实现自适应平滑系数的模型参数求解,采用梯度下降方法求解分割模型。采用本文算法对添加噪声的遥感图像进行分割实验,结果表明,所提算法可有效降低图像噪声的影响,同时可准确分割高分辨率遥感图像。     

衰减指数模型参数估计的新方法

推导出衰减指数模型的混合四阶累积量,它包含了准确的衰减频率、衰减因子及其他信息。在四阶混合累积量的基础上,提出了一种基于状态空间模型的谐波参数估计方法。先采用四阶混合累积量对观测信号进行预处理,再利用SSM方法实现衰减谐波信号各个参数的联合估计,实现了在计算量较低的情况下的衰减频率和衰减因子的联合估计。仿真结果表明,该方法能有效地抑制高斯色噪声,避免了在频域上搜索以辨识谱峰,具有优越的参数估计性能。     

动态融合梯度信息的Camshift算法改进研究

将梯度信息引入到Camshift算法之中,定义Camshift算法的梯度模型。依据运动目标和背景图像直方图的Bhattacharyya距离来动态决定梯度模型在查找算法中的决定权重,减小加入梯度后对算法时效性的影响;在Camshift算法求运动目标色调分量的过程中,改进由RGB空间到HSV空间转换计算的方法,减少反余弦和开方运算。在色调分量Hue基础上定义一种Hue分量,提高颜色空间之间的转换效率;在对目标跟踪框内颜色直方图进行计算时,以选取框重心位置为中心,距离中心越远的像素在颜色直方图中的比重越小.减小在选取运动物体初始位置时引入的背景噪声,提高跟踪算法的稳定性。实验证明:经过上述的改进,使得传统的Camshift算法在背景颜色与运动目标和有相似颜色物体对运动目标造成干扰的情况下的跟踪鲁棒性得到提高。     

基于熵图像和隶属度图的高斯混合背景模型

经典的高斯混合背景模型中,高斯分量的个数是固定的,近邻像素间的相关性也没有被考虑。作为对这种模型的改进,该文利用熵图像来度量背景像素亮度分布的复杂程度,进而给出了根据熵图像为各像素选择高斯函数个数的方法,在保证检测精度的前提下节约计算资源;并利用隶属度来表示像素属于背景的可能性,通过融合各像素邻域的局部信息来对其进行有效的分类,使得分类决策的结果更可靠,而计算量却增加不多。多种真实场景下的实验证明了这种算法在计算速度和精度上的良好性能。     

基于改进高斯混合模型的红外人体目标检测方法

针对高斯混合模型在阴影不显著情况下,容易把随光线突变而变化的背景像素点当作前景目标从而造成目标误检的缺点,提出了一种基于改进的高斯混合模型的红外人体目标检测方法。该方法引入边缘检测信息增强红外人体目标检测效果。首先,该算法利用Canny边缘检测来提取人体目标的边缘信息。然后,以此对每个像素建立高斯混合模型来完成人体目标的检测。实验结果表明,该方法能够有效消除光照突变所产生的阴影影响,提高了检测的准确性。     

基于空间约束高斯混合模型的EPLL自然图像复原

为了提高基于块先验的自然图像复原效果,有效去除图像中的噪声和模糊,提出了一种基于空间约束高斯混合模型的块似然对数期望(Expected Patch Log Likelihood, EPLL)复原框架。基于图像块的空间分布信息,将图像块的空间约束高斯混合统计特性作为先验,在图像块复原的基础上实现整幅图像的全局优化复原。对比相关的图像复原方法,提出的方法去噪和去模糊效果更好,并且保图像细节。利用客观性能指标对复原结果进行评价。实验结果表明,提出的方法有效易行,而且复原图像表现出良好的可视效果。     

A class-adaptive spatially variant mixture model for image segmentation.

We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.     

Double Gaussian mixture model for image segmentation with spatial relationships

In this paper, we present a finite mixture model based on a Gaussian distribution for image segmentation. There are four advantages to the proposed model. First, compared with the standard Gaussian mixture model (GMM), the proposed model effectively incorporates spatially relationships between the pixels using a Markov random field (MRF). Second, the proposed model is similar to GMM, but has a simple representation and is easier to implement than some existing models based on MRF. Third, the contextual mixing proportion of the proposed model is explicitly modelled as a probabilistic vector and can be obtained directly during the inference process. Finally, the expectation maximization algorithm and gradient descent approach are used to maximize the log-likelihood function and infer the unknown parameters of the proposed model. The performance of the proposed model at image segmentation is compared with some state-of-the-art models on various synthetic noisy grayscale images and real-world color images.     

Spatially constrained ICA algorithm with an application in EEG processing

Independent Component Analysis (ICA) aims at blindly decomposing a linear mixture of independent sources. It has lots of applications in diverse research areas. In some applications, there is prior knowledge on the sources and/or the mixing vectors. This prior knowledge can be incorporated in the computation of the independent sources. In this paper we provide an algorithm for so-called spatially constrained ICA (scICA). The algorithm deals with the situation when one mixing vector is exactly known. Also the generalization to more mixing vectors is discussed. Numerical experiments are reported that allow us to assess the improvement in accuracy that can be achieved with these algorithms compared to fully blind ICA and to a previously proposed constrained algorithm. We illustrate the approach with a biomedical application.     

Bayesian pixel classification using spatially variant finitemixtures and the generalized EM algorithm

A spatially variant finite mixture model is proposed for pixel labeling and image segmentation. For the case of spatially varying mixtures of Gaussian density functions with unknown means and variances, an expectation-maximization (EM) algorithm is derived for maximum likelihood estimation of the pixel labels and the parameters of the mixture densities, An a priori density function is formulated for the spatially variant mixture weights. A generalized EM algorithm for maximum a posteriori estimation of the pixel labels based upon these prior densities is derived. This algorithm incorporates a variation of gradient projection in the maximization step and the resulting algorithm takes the form of grouped coordinate ascent. Gaussian densities have been used for simplicity, but the algorithm can easily be modified to incorporate other appropriate models for the mixture model component densities. The accuracy of the algorithm is quantitatively evaluated through Monte Carlo simulation, and its performance is qualitatively assessed via experimental images from computerized tomography (CT) and magnetic resonance imaging (MRI).     

Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number

In view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.     

Spatially constrained sparse coding scheme for natural scene categorization

Coding and pooling, the major two sequential procedures in sparse coding based scene categorization systems, have drawn much attention in recent years. Yet improvements have been made for coding or pooling separately, this paper proposes a spatially constrained scheme for sparse coding on both steps. Specifically, we employ the m-nearest neighbors of a local feature in the image space to improve the consistency of coding. The benefit is that similar image features will be encoded with similar codewords, which reduced the stochasticity of a conventional coding strategy. We also show that the Viola–Jones algorithm, which is well-known in face detection, can be tailored to learning receptive fields, embedding the spatially constrained information on the pooling step. Extensive experiments on the UIUC sport event, 15 natural scenes and the Caltech 101 database suggests that scene categorization performance of several popular algorithms can be ubiquitously improved by incorporating the proposed two spatially constrained sparse coding scheme.     

Structural sparse coding seeds–active appearance model for object tracking

In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance model. The proposed object tracking scheme involves three stages: training, detection and tracking. In the training stage, an incremental SVM model that directly measures the candidates samples and target difference. The proposed mixture generate–discriminative method can well separate two highly correlated positive candidates images. In the detection stage, the trained weighted vector is used to separate the target object in positive candidates images with respect to the seeds images. In the tracking stage, we employ the particle filter to track the object through an appearance adaptive updating algorithm with seeds–active constrained sparse representation. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the current literature.     

改进的隐式空间映射算法的研究

介绍了一种快速收敛空间映射算法,改进了隐式空间映射算法中粗糙模型到精细模型之间参数映射。通过增加限定参数提取的方式,减少粗糙模型的参数空间而实现粗糙模型响应高效准确逼近精细模型响应。通过设计一个交叉耦合滤波器,与之前的隐式空间算法进行比较,更容易达到优化目标,证明了限定参数提取算法具有更快的逼近速度和更高的优化效率的优点。     

有限贝塔刘维尔混合模型的变分学习及其应用

由于贝塔刘维尔分布的共轭先验分布中存在积分表达式,贝叶斯估计有限贝塔刘维尔混合模型参数异常困难.本文提出利用变分贝叶斯学习模型参数,采用gamma分布作为近似的先验分布并使用合理的非线性近似技术,得到了后验分布的近似解.与常用的EM算法相比,该方法能够同时估计模型参数和确定分量数,且避免了过拟合的问题.在合成数据集及场景分类问题上进行了大量的实验,实验结果验证了本文所提方法的有效性.