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.

基于四叉树的ORB-LBP改进算法

针对ORB算法存在图像分布不均匀、匹配程度不高、匹配精度差的问题,通过划分网格计算图像灰度值的方法计算角点提取阈值。通过在金字塔层上构建四叉树的方法,在不同金字塔层分别构建不同深度的四叉树以提高计算效率,最后融合BRIEF-LBP特征描述子以提升ORB算法匹配精度。实验结果表明:对比传统ORB算法在速度上降低了5%,但均匀度提升了66左右,召回率也提升了10%;对比其他改进算法,速度提升了2%和5%,特征点分布均匀度提升了48和49,召回率也提升了36.63%和4.925%,实现了在少量增加计算量的同时,特征点均匀度和匹配精度效果有较大提升。

基于感兴趣区域的改进型LBP手指静脉识别

为进一步提升手指静脉识别算法的识别率,在图像预处理阶段提出一种快速感兴趣区域(RoI)提取方法,简化候选区域提取的计算过程,缩短手指区域提取时间。识别特征则是在局部二值模式(LBP)的基础上,利用邻域像素的平均值代替中心值,通过邻域像素的关系引入,提升了图像的纹理表达效果。在SDUMLA数据库与天津市智能实验室采集指静脉图像数据库上,分别取得了99.53%,99.74%的识别率,表明了算法优良的识别性能与泛化能力。

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.

基于LBP纹理与SegNet网络的灾损建筑物提取

高分辨率遥感影像中震后灾损建筑物提取是震害预估中极具重要的参考指标,研究遥感影像的震后灾损建筑物提取方法具有重要的科学意义。本文以青海玉树震后典型的灾损建筑物数据为研究对象,针对卷积神经网络对于城市建筑物纹理特征信息利用的不足,将局部二值模式(LBP)纹理特征与SegNet深度卷积神经网络相结合,采用有监督学习分类的方式训练卷积神经网络,实现震后灾损建筑物自动分类提取,并与传统面向对象提取方法进行对比。实验结果表明,LBP纹理特征与SegNet卷积神经网络模型相结合,对于震后灾损建筑物的提取能提高预测精度,用户精度与生产者精度分别有2%~7%,2%~9%的提升。

基于LBP和神经网络的织物疵点分类

织物疵点在销售中严重影响着产品的价格与品质,传统的织物疵点检测主要依靠人工检测,这种检测方式如今无法满足机器化时代下的高速度、高精度、高质量的要求。针对织物疵点检测难度大,效率低的问题,文章采用局部二值模式(LBP)和神经网络对织物疵点分类。首先,采用局部二值模式(LBP)对织物疵点纹理特征进行提取;其次,将特征值进行归一化处理并且将获得的特征值如能量、方差、熵等送入到已经训练好的BP神经网络中;最后,通过BP神经网络将前面送入的织物疵点特征值进行织物疵点先识别再分类;研究认为:基于局部二值模式和神经网络的织物疵点检测方法是一种可行的方法。该方法的平均准确率达到80%以上,平均召回率达到80%以上,分类的平均正确率达到85%以上。

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.

基于LBP的同图复制粘贴篡改图像的检验研究

铺天盖地的篡改图像已成为当今社会安全隐患之一。同图复制粘贴篡改是当前篡改图像中较为频繁使用的篡改手法,本文尝试基于LBP算法对同图复制粘贴篡改图像进行鉴别。先将图像转为灰度图再使用低通滤波操作将会降低检验效果的因素减少,再运用Harris算法提取特征点,LBP算法提取特征向量,并将特征值匹配,最后用RANSAC算法来消除误匹配点。结果显示,本文算法可有效检测出经过后处理操作过的同图复制粘贴篡改图像,该方法检验同图复制粘贴篡改图像效果良好。

复杂光照下LBP人脸识别算法的改进

针对复杂光照下人脸识别准确率低,改进了局部二值模式(Local Binary Patterns,LBP)算法。首先,以灰度对图像进行分层,在不同灰度层内提取LBP特征,根据某一分层上的特征分布及不同分层相同区域的特征相似度,确定该特征值的权重。其次,根据特征值的权重,动态调整尺度变换的窗口大小,合并权重较高的特征,减裁权重较低和无效特征,并构成新的直方图进行对比识别。最后,进行实验对比分析。实验结果表明,该算法可以有效地减少不同尺度变换中的特征计算量,且在复杂光照条件下更加有效地保留细节特征信息,减少干扰特征。

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.

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。

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。

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。

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。