A low light natural image statistical model for joint contrast enhancement and denoising

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods.     

基于图切割的人体运动检测

研究利用图切割对人体进行有效检测的方法.首先在色相、饱和度和亮度(HSV)颜色空间建立自适应的背景混合模型快速提取背景;然后计算差分并消除阴影;最后构造8连通网络图,使用最小切割完成目标的分割.通过实验,对单模型与混合模型背景、4连通与8连通邻域以及基于数学形态学与基于图切割的分割进行了比较.结果表明,在实际环境下,采用本方法可快速、有效和鲁棒地对人体运动进行检测,并获得干净、光滑的分割结果.     

含La或Sm的BaNd_(2-y)R_yTi_(4+x)O_(12+2x)瓷的结构与性能

ＢａＯ－Ｎｄ２Ｏ３－ＴｉＯ２系富钛区含Ｌａ或Ｓｍ的陶瓷,ＸＲＤ、ＳＥＭ显微结构分析表明,其主晶相为Ｒ（Ｒ＝Ｌａ或Ｓｍ）取代Ｎｄ固溶体ＢａＮｄ２－ｙＲｙＴｉ５Ｏ１４和ＢａＮｄ２－ｙＲｙＴｉ４Ｏ１２,均属斜方晶系,并存在少量的次晶相Ｒ２Ｔｉ２Ｏ７、ＢａＴｉ４Ｏ９、Ｂａ２Ｔｉ９Ｏ２０等。富钛区ＢａＮｄ２Ｔｉ４＋ｘＯ１２＋２ｘ（ｘ＝０～１．４００）组分和含Ｌａ或Ｓｍ的ＢａＮｄ２－ｙＲｙＴｉ５Ｏ１４组分所组成的瓷料,介质损耗显著地降低,介电常数和电容温度系数遵从李赫涅德凯对数混合定律。     

基于熵调整模糊c-均值聚类的时频能量混合模型

本文提出了一种改进由时频不相交分量组成信号的双线性时频分布的分辨率和可读性的方法。用修正的Xie-Beni聚类有效性指标对熵调整模糊c-均值聚类算法进行拓展将模糊聚类与密度估计相结合,实现了信号时频分量的识别和建模;信号的时频能量混合模型给出了信号分量的数目及其在时频面上所占据的区域。这些信息可以用于分离信号分量,设计适合于每个分离分量的光滑核。仿真结果表明,对于由时频不相交分量组成的信号,本方法可以识别出其中的信号分量,并得到较优的时频分布。     

利用性别判识技术的说话人识别系统的研究

本文在基于GMM的与文本无关说话人识别系统中考虑性别差异,提出了把不同性别的说话人特征参数归成不同的两个子集,识别时先判断待识别说话人特征是属于哪个性别子集后,再在相应的子集中进行识别的方法。实验结果表明该方法能够改善基于GMM的与文本无关说话人识别系统的识别率。     

Efficient SRAM yield optimization with mixture surrogate modeling

Largely repeated cells such as SRAM cells usually require extremely low failure-rate to ensure a moderate chi yield. Though fast Monte Carlo methods such as importance sampling and its variants can be used for yield estimation, they are still very expensive if one needs to perform optimization based on such estimations. Typically the process of yield calculation requires a lot of SPICE simulation. The circuit SPICE simulation analysis accounted for the largest proportion of time in the process yield calculation. In the paper, a new method is proposed to address this issue. The key idea is to establish an efficient mixture surrogate model. The surrogate model is based on the design variables and process variables. This model construction method is based on the SPICE simulation to get a certain amount of sample points, these points are trained for mixture surrogate model by the lasso algorithm. Experimental results show that the proposed model is able to calculate accurate yield successfully and it brings significant speed ups to the calculation of failure rate. Based on the model, we made a further accelerated algorithm to further enhance the speed of the yield calculation. It is suitable for high-dimensional process variables and multi-performance applications.     

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.     

基于树结构分层随机森林在非约束环境下的头部姿态估计

头部姿态估计是人类行为和注意力的关键,受到光照、噪声、身份、遮挡等许多因素的影响。为了提高非约束环境下的估计准确率和鲁棒性,该论文提出了树结构分层随机森林在非约束环境下的多类头部姿态估计。首先,为了消除不同环境的噪声影响,提取人脸区域的组合纹理特征,对人脸区域进行积极人脸子区域的分类,分类结果作为树结构分层随机森林的先验知识输入;其次,提出了一种树结构分层随机森林算法,分层估计多自由度下的头部姿态;再次,为了增强算法的分类能力,使用自适应高斯混合模型作为多层次子森林叶子节点的投票模型。在多个公共数据集上的多种非约束实验环境下进行头部姿态估计,最终实验结果表明所提算法在不同质量的图像上都有很好的估计准确率和鲁棒性。     

高浓度固液混合流的湍流模型

建立了高浓度固液混合流的湍流模型，分析了混合流的应力（包括颗粒碰撞应力、粘性应力、湍流应力及Ｃｏｕｌｏｍｂｉｃ摩擦应力）。根据高浓度混合流中颗粒的受力，得出了颗粒运动的力平衡方程。由于引入颗粒碰撞应力和Ｃｏｕｌｏｍｂｉｃ摩擦应力，本模型较其它模型更适合具有高浓度和大颗粒的流场。利用本模型模拟的高浓度含沙水流在水平管道中的流动特性，其数值结果与前人所做的实验结果较一致。