加权信息熵与增强局部二值模式结合的人脸识别

针对人脸识别因光照、姿态、表情、遮挡及噪声等多种因素的影响而导致的识别率不高的问题，提出一种加权信息熵（IEw）与自适应阈值环形局部二值模式（ATRLBP）算子相结合的人脸识别方法（IE （w） ATR-LBP）。首先，从原始人脸图像分块提取信息熵，得到每个子块的IEw；然后，利用ATRLBP算子分别对每个人脸子块提取特征从而得到概率直方图；最后，将各个块的IEw与概率直方图相乘，再串联成为原始人脸图像最后的特征直方图，并利用支持向量机（SVM）对人脸进行识别。在AR人脸库的表情、光照、遮挡A和遮挡B四个数据集上，IE （w） ATR-LBP方法分别取得了98.37%、94.17%、98.20%和99.34%的识别率。在ORL人脸库上，IE （w） ATR-LBP方法的最大识别率为99.85%；而且在ORL人脸库5次不同训练样本的实验中，与无噪声时相比，加入高斯和椒盐噪声后的平均识别率分别下降了14.04和2.95个百分点。实验结果表明，IE （w） ATR-LBP方法能够有效提高人脸在受光照、姿态、遮挡等影响时的识别率，尤其是存在表情变化及脉冲类噪声干扰时的识别率。     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

人脸识别技术综述

文章首先对人脸识别技术进行了介绍，其次回顾了人脸识别研究的发展历程及识别方法的基本分类，然后对当前主流的人脸识别方法展开了详细的论述，最后提出了人脸识别技术面临的问题及研究方向。     

No reference image blurriness assessment with local binary patterns

In this paper, we put forward an effective and efficient no reference image blurriness assessment metric on the basis of local binary pattern (LBP) features. In this proposal, we reveal that part of the LBP histogram bins present monotonously with the degree of blurriness. The proposed method contains the following steps. Firstly, the LBP maps of an input image are extracted with multiple radiuses. And then, the frequency of pattern histogram is analyzed before part of bins are chosen as the features. In addition, we also take the entropy of these bins as another feature. Finally, we learn the extracted features to predict the image blurriness score. Validation of the proposed method is conducted on the blurred images of LIVE-II, CSIQ, TID2008, TID2013, LIVE3D IQA Phase I and LIVE3D IQA Phase II. Experimental results demonstrate that compared with the state-of-the-art image quality assessment (IQA) methods, the proposed algorithm has notable advantage in correlation with subjective perception and computational complexity.     

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS−MRI database which is the magnetic resonance imaging (MRI) database and VIA/I–ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.     

基于LBP的人脸识别

本文首次提出应用局部二值模式(LBP)算法来预测人脸旋转姿态。首先建立平均人脸的特征直方图模型(正面人脸特征直方图、左转人脸特征直方图和右转人脸特征直方图),然后通过比较测试人脸特征直方图与平均人脸特征直方图的距离来预测人脸旋转类型。实验结果表明本文方法可以有效的预测人脸姿态。     

针对MME的JPEG图像隐写分析方法研究

MME（Modified Matrix Encoding）作为一种新兴的JPEG图像隐写技术,具有隐写量大、对图片的改动小、抗检测性优于传统隐写算法等优点。文中在采用Markov特征的基础上,加入了能够更好描述图像局部纹理的局部二值模式（LBP）与局部顺序对比模式（LOCP）特征,并应用Ensemble分类器完成分类和识别。通过对UCID图像库的大量实验,得到了一种针对MME算法的最优特征组合。相比传统的隐写分析方法,文中所提出的方法具有更好的检测正确率。     

基于鲁棒稀疏表示的人脸识别算法

提出了一种基于LBP算子和鲁棒稀疏表示的人脸识别方法。首先,提取训练样本和测试样本的LBP特征。其次,在原有稀疏表示分类器(SRC)的基础上添加一个权值矩阵W来解决l1正则化最小二乘问题。最后,利用鲁棒稀疏表示分类器(RSRC)分类测试人脸图像所属类别。在ATT人脸库上进行实验的结果表明,此方法是优于其他经典算法的。