Local binary pattern-based reversible data hiding

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

A Novel Tracking-by-Detection Method with Local Binary Pattern and Kalman Filter

Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experiments show that under some scenarios,such as non-uniform illumination changing,serious occlusion,or motion-blurred,it may fails to track the object. In this paper,to surmount some of these shortages,especially for the non-uniform illumination changing,and give full play to the performance of the tracking-learning-detection framework, we integrate the local binary pattern( LBP) with the cascade classifiers,and define a new classifier named ULBP( Uniform Local Binary Pattern) classifiers. When the object appearance has rich texture features,the ULBP classifier will work instead of the nearest neighbor classifier in TLD algorithm,and a recognition module is designed to choose the suitable classifier between the original nearest neighbor( NN) classifier and the ULBP classifier. To further decrease the computing load of the proposed tracking approach,Kalman filter is applied to predict the searching range of the tracking object.A comprehensive study has been conducted to confirm the effectiveness of the proposed algorithm (TLD _ULBP),and different multi-property datasets were used. The quantitative evaluations show a significant improvement over the original TLD,especially in various lighting case.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain

Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.

使用多尺度LBP特征描述与识别人脸

提出了一种基于多尺度LBP特征的人脸描述与识别算法。对原始人脸图像进行二级小波分解,并采用LBP算子分别计算两幅低频逼近图像的LBP特征谱;将LBP特征谱划分为若干个互不重叠的特征区域,然后分别进行直方图统计。最后,将所有区域的LBP直方图序列连接起来得到多尺度LBP特征,将其作为人脸的鉴别特征用于分类识别。所提出算法在ORL人脸数据库中取得了高达99%的人脸识别率。实验分析表明,多尺度LBP特征具有较强的人脸图像描述能力和可鉴别性,且对人脸表情及位置的变化具有较高的鲁棒性。

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.

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。

多级LBP直方图序列特征的人脸识别

人脸识别是当前人工智能和模式识别的研究热点。基于对小波分解和局部二进制模式(LBP)分析,提出了一种多级LBP直方图的序列特征(M-HSLBP)的提取方法。2维的小波分解具有对表情变化不敏感的特点,可以很好地压缩和表征人脸图像的特征;LBP是一种有效的纹理描述算子,使用多级可变大小的子窗口对小波变换后的图像进行扫描,对各级子图像进行改进LBP变换并形成多级LBP直方图序列特征,这种特征既能反映人脸局部特征又能反映其整体特征。径向基网络作为分类器具有很高的推广性能,有利于大容量样本的分类。在对人脸库ORL和YEL的识别实验中,该算法识别率达到98%以上,与传统算法相比,取得了更好的识别结果。

基于LBP特征和稀疏表示的新生儿疼痛表情识别

面部表情被认为是新生儿疼痛评估的可靠指标。文中提出一种基于加权局部二元模式(LBP)特征描述符和稀疏表示分类器的新生儿疼痛表情识别方法。首先,经归一化后的面部图像采用一个特征向量来描述,这个特征向量是通过串接组合所有局部图像块的LBP特征图的加权直方图所得到的直方图序列。然后,采用主成分分析(PCA)方法对训练样本及测试样本图像的特征向量进行降维。最后,采用基于稀疏表示的分类器将测试样本图像的表情分为4类:平静、哭、轻度疼痛、剧烈疼痛。文中研究目的是通过利用基于计算机的图像分析技术来辅助临床医生评估新生儿疼痛。在新生儿面部图像数据库上进行的实验结果表明了该算法的有效性,表情分类的平均识别率高达84.50%。

DCT和LBP特征融合的人脸识别

提出一种特征融合的人脸识别新方法.该方法将人脸图像中少量的低频离散余弦变换(DCT)系数用作人脸的频域特征;把人脸图像规则地分成多个子块,计算每个子块的局部二值图(LBP)编码直方图.这些子块的LBP直方图连接成一个空域全局直方图,作为人脸的描述向量.这个描述向量经过PCA降维后作为人脸的LBP特征.DCT特征和LBP特征分别归一化,然后进行特征融合.在ORL人脸库上的实验显示了所提方法比单独采用DCT或LBP特征的人脸识别有较好的性能改善.

人脸表情的LBP特征分析

为了有效提取面部表情特征,提出了一种新的基于LBP(局部二值模式)特征的人脸表情识别特征提取方法。首先用均值方差法对表情图像进行灰度规一化,通过对图像进行积分投影,定位出眉毛、眼睛、鼻和嘴巴这些关键特征点,进而划分出各特征部件所在子区域,然后对子区域进行分块,提取各个子区域的分块LBP直方图特征。为了验证所提出的方法的合理性,最后在JAFFE表情库上进行了实验,结果表明提出的方法能够有效地描述表情的特征。

LMCP:用于变化光照下人脸识别的LBP改进方法

LBP算子是在人脸识别和纹理分析领域比较成功的一种方法,但是由于没有考虑像素值之间的对比度,因而丢弃掉了重要的纹理特征。提出了一种LMCP方法,解决了LBP方法的这个缺点。该方法先通过预处理,将光照变化控制在一定范围内,然后求得局部区域中心像素点和邻居像素点之间的对比度值,并将其最大值和最小值之间的值域划分为若干个层次,将每个对比度值映射到某个层次上,再使用LBP类似方法获得若干个数值组合而成的LMCP特征值。此外,还使用了统计映射的方法进行降维。实验结果证明了LMCP方法比LBP方法更加有效。

改进的HOG和Gabor,LBP性能比较

为了实现复杂环境下的人脸特征有效表达,提出一种改进的梯度方向直方图(HOG)人脸识别方法.首先以人脸图像网格作为采样窗口并在其上提取HOG特征;然后将所有网格HOG特征向量进行组合,实现整个人脸特征表达;最后采用最近邻分类器进行识别.另外,比较了该方法与Gabor小波和局部二值模式(LBP)2种著名的人脸局部特征表示方法的优劣.实验结果表明,在调优的HOG参数下,在具有光照和时间环境等复杂变化的FERET人脸库中,较少维数的HOG特征比LBP特征有更好的表现,而且HOG特征提取时间和特征向量维数比Gabor小波方法更具有优势.

融合LBP和GLCM的纹理特征提取方法

为提取有效的特征用于纹理描述和分类,提出一种融合局部二进制模式(LBP)和灰度共生矩阵(GLCM)的纹理特征提取方法。利用旋转不变的LBP算子处理纹理图像,得到LBP图像及其GLCM,采用对比度、相关性、能量和逆差矩描述图像的纹理特征。实验结果表明,与其他方法相比,该方法提取的纹理特征具有更强的纹理鉴别能力,平均分类正确率达到93%。

基于颜色和LBP多特征的mean shift的跟踪算法

mean shift目标跟踪使用任一个单独特征都很难对大幅度的光照、背景变化和车辆大范围运动具有强鲁棒性,如单一的颜色特征对光照较为敏感,光照突变容易导致目标丢失。当背景颜色和目标颜色相近时也易造成目标丢失,因此利用目标的单一特征信息往往很难实现鲁棒的目标跟踪。文章提出基于颜色和LBP多特征mean shift跟踪方法,使跟踪结果不再过分依赖某一特征,增强了对背景变化、目标大范围运动的鲁棒性。

融合LBP和局部稀疏表示的人脸表情识别

为更好获取人脸局部表情特征,提出了一种融合局部二值模式(Local Binary Pattern,LBP)和局部稀疏表示的人脸表情特征与识别方法。为深入分析表情对人脸子区域的影响,根据五官特征对人脸进行非均匀分区,并提取局部LBP特征;为精细刻画人脸局部纹理,整合人脸局部特征,设计了人脸局部稀疏重构表示方法,并根据表情对各局部子区域的影响因子,加权融合局部重构残差进行人脸表情识别。在JAFFE2表情人脸库上的对比实验,验证了该方法的可行性和鲁棒性。

结合改进的LBP和LDP的人脸表情识别

提出了一种结合改进的LBP(局部二值模式)和LDP(局部定向模式)的人脸表情特征提取方法。改进的LBP维数明显降低,更多地考虑了空间结构信息且计算速度得到了提高。LDP方法具有很强的抗噪能力,更好地提取边缘信息。基于两种方法可以优势互补,先对图像分块子区域用改进的LBP进行特征提取,再用LDP对脸部子区域进行特征提取,最后把改进的LBP提取的特征和LDP提取的特征按顺序串接起来作为总特征,用最近邻进行分类。在JAFFE表情库进行了实验,证明提出的方法能够有效地提高人脸表情的识别率。

基于LBP-TOP特征的微表情识别

微表情是一个人试图隐藏内心真实情感却又不由自主流露出的不易被察觉的面部表情。与一般面部表情相比,微表情最显著的特点是持续时间短、强度弱,往往难以有效识别。文中提出了一种基于LBP-TOP(Local Binary Pattern from Three Orthogonal Planes)特征和支持向量机(Support Vector Machine,SVM)分类器的微表情识别方法。首先,采用LBP-TOP算子来提取微表情特征;然后,提出一种基于ReliefF与局部线性嵌入(Locally Linear Embedding,LLE)流形学习算法相结合的特征选择算法,对提取的LBP-TOP特征向量进行降维;最后,使用径向基函数(Radial Basis Function,RBF)核的SVM分类器进行分类,将测试样本图像序列的微表情分为5类:高兴、厌恶、压抑、惊讶、其他。在CASME Ⅱ微表情数据库上采用"留一人交叉验证"(Leave-One-Subject-Out Cross Validation,LOSO-CV)的方式进行了实验,可得到58.98%的分类准确率。实验结果表明了该算法的有效性。

基于改进LBP的手指静脉识别算法

为进一步提高手指静脉识别算法识别率,在图像预处理阶段提出了将直方图均衡化算法与局部对比度增强算法相结合,在扩大静脉图像动态范围的同时增强图像的细节。在图像识别阶段,提出了一种基于局部二值模式(Local Binary Pattern,LBP)的改进算子用来描述图像局部纹理特征,并结合权重分配和分块LBP特征算法对图像进行特征提取。最后使用支持向量机(Support Vector Machine,SVM)算法对图像特征进行训练。实验结果表明,上述算法识别率高达99.33%,与传统的识别算法相比具有明显的准确率提高。