An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

Android Malware Detection Using Local Binary Pattern and Principal Component Analysis

Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

基于全局LBF水平集模型的脑血管层次粗分割

考虑对脑血管进行三维分割具有一定难度,提出了一种基于全局LBF(Local Binary Fitting)水平集模型的脑血管层次化粗分割方法。首先,应用定向加权中值(DWM)滤波和各向异性扩散滤波去除脑图像噪声,同时保存血管边缘信息,在多尺度条件下局部梯度最大(LIGM)算法,应用灰度和梯度信息提取备选血管,基本实现脑灰质去除。然后,改进全局信息LBF水平集算法实现最大强度投影(MIP)图像分割,采用形态信息提取备选血管,剔除干扰组织。最后,融合两种方法实现脑血管粗提取。实验表明,层次化的分割方法可去除大部分不相关脑组织,包含直接双高斯统计模型中的所有分割血管信息。本项研究基于时飞磁共振血管造影(TOF_MRA)数据,相关研究结果可扩展到其它相似系统中。

LBF活动轮廓模型的改进

LBF模型是一个著名的基于区域的活动轮廓模型。与PC(Piecewise Constant)模型不同,该模型引入了一个以高斯函数为核函数的局部二值拟合(Local Binary Fitting,LBF)能量。因为LBF能量能够获取图像的局部信息,所以LBF模型解决了PC模型不能处理灰度不均一图像的分割问题。提出了一个改进的LBF模型,它使用一个新的核函数代替高斯核函数。实验表明:与LBF模型比较,新模型减少分割时间约50%。

基于FCM和LBF模型的医学图像自动分割

基于局部区域二相拟合(LBF)模型的医学图像分割方法,对初始轮廓敏感并仅能分割单类目标,若手动选取的初始轮廓不合适,将导致算法耗时过大甚至分割失败。针对上述不足,提出联合模糊C均值(FCM)聚类的LBF模型自动分割算法。对待分割图像进行FCM聚类,将得到的目标类隶属度值变换为适用于LBF模型的水平集函数初始值,利用LBF模型从该初始值开始演化直至收敛,从而完成分割。合成图像及血管和脑部图像的分割实验结果表明,该算法能够自动获取合适的初始值,有效解决LBF模型对初始轮廓敏感的问题,减少迭代次数,而且通过选择不同的FCM聚类结果,可以实现对多类目标的分割。

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.

基于LBF的人脸视频心率测量

心率是衡量人体健康和心理状况的重要指标,针对目前基于人脸视频的非接触式心率信号的提取易受人脸倾斜的影响,提出了一种基于局部二值特征的人脸视频心率测量方法。首先对使用普通摄像头采集的人脸视频进行人脸检测,运用局部二值特征模型实现人脸对齐,在面部区域精确选取感兴趣区域(ROI);其次在HSL颜色空间下对ROI进行皮肤分割,利用色度模型进一步去除脸部运动的干扰,并通过巴特沃斯带通滤波器减少噪声影响,提取脉搏信号;最后对提取的脉搏信号利用离散傅里叶变换进行时频分析得到心率值。实验结果表明,基于局部二值特征的人脸视频心率测量方法在人脸倾斜的情况下仍有较好的准确性和稳定性,平均绝对误差为2.2050,均方根误差为2.6395,与指尖脉搏血氧仪测量法具有很好的一致性,与其他方法比较性能有所提升,能够满足日常生活和远程医疗中心率测量的需要。

An automated detection of glaucoma using histogram features

Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.

Product Image Classification Based on Fusion Features

Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.

一种基于LC-BoVW模型的建筑垃圾物料识别算法

针对建筑垃圾物料的种类多、形貌易混淆等问题,构建了一种基于局部约束的视觉词袋(local constraint-bag of visual words,LC-BoVW)模型的建筑垃圾物料识别算法。首先,对建筑垃圾物料图像分块,分别提取局部颜色特征和局部二值模式特征;考虑到图像分块特征的局部相似特性,构建LC-BoVW模型分别对目标图像的显著特征进行统计。然后,基于信息融合思想对特征统计量进行融合,形成图像的判别性特征并输入到分类器中进行物料的精确识别。最后,利用自建的5类建筑垃圾物料图像数据集进行实验,实验结果表明,所提算法能够快速有效地实现建筑垃圾物料识别,平均识别准确率可达到97.92%。

基于多频特征和纹理增强的轻量化图像超分辨率重建

现有基于卷积神经网络主要关注图像重构的精度,忽略了过度参数化、特征提取不充分以及计算资源浪费等问题。针对上述问题,提出了一种轻量级多频率特征提取网络(MFEN),设计了轻量化晶格信息交互结构,利用通道分割和多模式卷积组合减少参数量;通过分离图像的低频、中频以及高频率信息后进行特征异构提取,提高网络的表达能力和特征区分性,使其更注重纹理细节特征的复原,并合理分配计算资源。此外,在网络内部融合局部二值模式(LBP)算法用于增强网络对纹理感知的敏感度,旨在进一步提高网络对细节的提取能力。经验证,该方法在复杂度和性能之间取得了良好的权衡,即实现轻量有效提取图像特征的同时重建出高分辨率图像。在Set5数据集上的2倍放大实验结果最终表明,相比较于基于卷积神经网络的图像超分辨率经典算法(SRCNN)和较新算法(MADNet),所提方法的峰值信噪比(PSNR)分别提升了1.31 dB和0.12 dB,参数量相比MADNet减少了55%。

一种高效的二进制点云局部特征描述算法

针对现有点云局部特征描述算法时效性和紧凑型不能满足实际应用需求的问题,提出一种二进制多层切片距离特征描述算法(B-MSD)。首先在特征点处建立了一种稳健的局部参考坐标系;然后设计了两种多层切片和子区域划分方式并计算平均距离,以提取点云局部三维形状特征;最后通过量化的方法将各子区域计算的平均距离转化为二进制码串,串联所有码串生成最终的二进制描述子。在多个公开点云数据集上与几个经典描述子进行仿真对比,结果表明,在鉴别力更强的基础上,上述算法的紧凑性、计算效率和匹配速度方面综合表现更好。3D目标检测和点云配准应用实验也验证了上述算法的有效性。

基于信息熵的二维局部二值模式静脉识别

基于现有LBP算法及其变体无法提取图像高维特征的问题,提出一种基于信息熵的二维局部二值模式识别算法。此方法首先利用统一局部二值模式(ULBP)对图像进行低维特征的提取,随后将图像信息熵与统一局部二值模式图谱进行结合获取熵值加权的统一局部二值模式图谱(EULBP),并利用滑动窗口实现对局部区域内模式间共现特征信息的统计,以其结果作为图像特征表达。并以直方图交叉距离为基础构建模式分类器,验证其识别性能。实验结果表明,在SDUMLA⁃HMT数据集以及马来西亚理工大学指静脉数据集(FV⁃USM)中,提出的算法能取得99.94%和98.84%的平均识别率。

基于LBP值对空间统计特征的纹理描述符

针对基于内容图像检索应用背景下局部二值模式(LBP)描述符缺乏空间描述能力及所需特征矢量维数较长的不足,提出一种基于LBP值对空间统计特征构建的改进纹理描述符(ILBP).ILBP描述符首先利用LBP微模式编码方法将原始图像转换为LBP伪灰度图像,然后再提取出多个关于LBP值对空间分布关系统计值构成描述图像特征的特征矢量.在基于内容的图像检索原型测试平台上完成大量实验.实验结果表明,与LBP及其各类变种描述符相比,ILBP描述符在进一步增强LBP描述符描述能力的同时大幅度压缩特征矢量维数,具有更好的查询正确率和查询效率.

局部二值模式方法研究与展望

针对当前局部二值模式(Local binary pattern,LBP)方法表现出的理论和实际应用价值,系统综述了在纹理分析和分类、人脸分析和识别以及其他检测与应用中的各种LBP方法.首先,简要概述了LBP方法的原理,主要分析了LBP方法中的阈值操作并介绍了统一模式和旋转不变性模式.其次,分别对纹理分析和分类中的LBP方法、人脸分析和识别中的LBP方法以及其他检测与应用中的LBP方法等三个方面进行了详细的梳理和评述.最后,分析了LBP方法在应用中依旧存在的重要问题并指出了未来的研究方向.

面向语音情感识别的语谱图特征提取算法

为研究信号相关性在语音情感识别中的作用,提出了一种面向语音情感识别的语谱图特征提取算法.首先,对语谱图进行处理,得到归一化后的语谱图灰度图像;然后,计算不同尺度、不同方向的Gabor图谱,并采用局部二值模式提取Gabor图谱的纹理特征;最后,将不同尺度、不同方向Gabor图谱提取到的局部二值模式特征进行级联,作为一种新的语音情感特征进行情感识别.柏林库(EMO-DB)及FAU Ai Bo库上的实验结果表明:与已有的韵律、频域、音质特征相比,所提特征的识别率提升3%以上;与声学特征融合后,所提特征的识别率较早期声学特征至少提高5%.因此,利用这种新的语音情感特征可以有效识别不同种类的情感语音.

基于完整LBP特征的人脸识别

提出一种基于完整局部二值模式(CLBP)进行人脸识别的方法,CLBP算子包括三个部分:中心像素的LBP(CLBP_C)、符号部分的LBP(CLBP_S)、数值部分的LBP(CLBP_M)。该方法首先采用CLBP算子提取人脸灰度图像的直方图;然后融合成CLBP直方图,进行直方图相似性比较;最后根据最近邻原则进行识别。在ORL和YALE标准人脸数据库上的实验表明,该方法得到的结果比LBP效果更好,鲁棒性更高。

LBP和HOG的分层特征融合的人脸识别

针对LBP描述子提取的纹理特征有限且不能有效地描述图像边缘和方向信息的问题,提出了LBP和HOG的分层特征融合的方法.首先利用LBP算子提取图像的分层纹理谱特征,然后利用HOG算子提取原始图像的边缘特征和基于分层LBP特征的分层HOG特征,最后将分层LBP特征分别与2种HOG边缘特征融合,得到2种不同的多层融合特征.通过在ORL,Yale和GT人脸库上进行实验,比较了15种算法的识别性能,结果证明了文中方法的有效性;相对于传统的经典降维算法、单一的LBP特征提取算法和HOG特征提取算法,该方法的识别率有很大的提高,分别达到99%,99.5%和99.14%.

用于纹理特征提取的改进的LBP算法

针对现有的纹理特征提取方法计算复杂度高的问题,利用局部二值模式(LBP)算法思想简单、计算复杂度小的优势,在已有的完整LBP(CLBP)算法基础上,提出了一种改进的CLBP算法(ICLBP)。ICLBP算法保留了CLBP算法中CLBP_S,而对CLBP_M算子、CLBP_C算子进行了改进,提出一个新的纹理描述算子ICLBP_T。ICLBP算法更全面地描述了局部窗口的纹理特征,同时有效解决了CLBP算法中CLBP_M算子对灰度分布不均敏感的问题。通过对Outex、CURet数据库的数据分类实验,结果表明,相比于已有的LBP算法,ICLBP算法的分类精度有了明显的改进,同时ICLBP算法中ICLBP_SCT特征具有较低的特征维数,具有较好的实用价值。