Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

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.

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.

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.

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.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

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.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

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.

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%.

Palmprint Recognition Based on Statistical Local Binary Orientation Code

A novel coding based method named as local binary orientation code (LBOCode) for palmprint recognition is proposed.The palmprint image is firstly convolved with a bank of Gabor filters,and then the orientation information is attained with a winner-take-all rule.Subsequently,the resulting orientation mapping array is operated by uniform local binary pattern.Accordingly,LBOCode image is achieved which contains palmprint orientation information in pixel level.Further we divide the LBOCode image into several equal-size and nonoverlapping regions,and extract the statistical code histogram from each region independently,which builds a global description of palmprint in regional level.In matching stage,the matching score between two palmprints is achieved by calculating the two spatial enhanced histograms' dissimilarity,which brings the benefit of computational simplicity.Experimental results demonstrate that the proposed method achieves more promising recognition performance compared with that of several state-of-the-art methods.

基于特征提取和图像分类的螺旋网疵点自动检测

为了解决当前螺旋网人工疵点检测效率低、误检率高等问题,提出了一种基于分类思想的螺旋网疵点检测方法。对螺旋网图像提取多模式多尺度的LBP特征,充分表征螺旋网图像的信息,通过构建支持向量机(Support vector machine,SVM)分类器实现螺旋网疵点自动检测。结果表明:对于螺旋网疵点图像的局部二值模式(Local binary pattern,LBP)特征,采样半径为2,采样点个数为8时的均匀模式LBP的分类准确率优于其他模式和尺度的LBP,达到了100%,检测速度为0.48 s/张。通过对比不同的特征提取方法和分类器,验证了该文方法对于螺旋网疵点自动检测的适用性,可以实现纺织企业中螺旋网的自动化检测。

基于LBP和注意力机制的改进VGG网络的人脸表情识别方法

为了提高训练速度和人脸表情识别效果,提出一种基于局部二值模式(Local Binary Pattern,LBP)和注意力机制的改进视觉几何群网络(Visual Geometry Group Network,VGG网络)的人脸表情识别方法。首先,通过LBP获取数据集的纹理特征。其次,利用全局平均池化层代替全连接层,并在基准模型卷积层后和全局平均池化层前引入注意力模块,创建新网络模型NEW-VGG;通过对NEW-VGG做消融实验,验证模型改进的正确性。最后,融合LBP+NEW-VGG模型对CK+和Fer2013两种数据集进行10倍交叉验证,取得了97.98%和76.75%的识别率。实验结果表明,该方法不仅能加快网络训练迭代速度,增强人脸表情识别效果,还具有较强的鲁棒性。

中国象棋自动打谱方法研究

针对现存象棋打谱方式繁琐、成本较高的问题,提出了一种基于机器视觉的象棋自动打谱方法。对图像进行预处理后,首先结合二值化与连通区域搜索进行人手遮挡检测,随后采用Hough圆检测、字符矩阵等方法对棋子进行定位,接着将棋子分为红黑两方,并利用局部二进制模式直方图(local binary pattern histogram,LBPH)算法实现棋种识别,最后通过动态识别棋子移动路径,根据行棋规则生成着法。选取50局象棋比赛录像进行测试,结果表明,该方法在识别准确率达到99%的前提下,1 s内可对5帧图像进行处理与识别,且对50个视频识别得到的棋谱正确率均为100%,可以完全满足各类型对局的打谱需求。

球形全景核线约束的颜色不变量综合特征快速匹配方法

针对目前立体全景模型量测应用中特征匹配自动化程度偏低的问题,提出一种基于立体球形全景约束的颜色不变量综合特征快速匹配方法.首先,通过核线约束,使全景影像匹配的搜索范围从二维限制到一维带状缓冲区域;然后利用颜色不变量相关系数进一步确定精细搜索范围;最后,基于颜色不变量及旋转不变纹理特征构建综合匹配测度模型,以实现全景特征匹配.通过实地拍摄的全景图像,与灰度匹配法、SIFT,SURF以及CSIFT进行比较分析.实验结果表明,该方法匹配准确率提高了近10%,可消减球形全景特征的误匹配,有效地解决影像匹配时同名像点自动寻找与几何信息快速解算,为全景量测模型二次采样、量测及深度图的生成奠定基础.

局部特征映射与融合网络的人脸识别优化算法

针对传统特征提取算法的局限性,提出基于深度神经网络DeepLab v2的人脸识别改进算法。首先,对图像中人脸进行定位,采用DeepLab v2改进网络提取人脸的面部特征,通过加入压缩激励(SE)模块细化多角度纹理特征。其次,采用局部二值模式(LBP)特征映射对目标图像进行补充特征提取,细化纹理结构并减少光照噪声的干扰,提升识别的鲁棒性。最后,进行特征信息融合,采用分类模块对融合特征识别并分类处理。结果表明:对比经典目标检测算法YOLOv1和传统DeepLab算法,改进算法识别出多角度的人脸局部特征,且在正常光照下改进算法的识别精确度分别提高了3.1%和5.9%,在强光照下改进算法的识别精确度分别提高了9.5%和13.6%。

面向虹膜识别的多方向中心对称局部二值模式

为了提升虹膜纹理的表征效果,获得稳定的局部纹理信息表达,提出基于多方向中心对称局部二值模式的虹膜识别算法.首先通过眼睑边缘快速定位生成掩膜,抑制眼睑区域噪声;然后在多方向的基础上,提出多方向中心对称的局部二值模式完成虹膜纹理特征的表征;最后利用汉明距离判定虹膜是否匹配.针对由于类内与类间匹配次数失衡导致评价指标正确识别率存在的不足,提出修正的正确识别率.在CASIA.V1,CASIA.V3-Interval,JUL6.0和CASIA.V4-Lamp虹膜库上的实验结果表明,所提算法的修正的正确识别率分别为99.857%,99.940%,99.640%和97.973%,等错误率下降0.03%~5.96%;该算法能够有效地表征虹膜纹理信息,具有良好的识别性能和鲁棒性.

基于非线性扩散和多特征融合的提花针织物疵点检测

为解决提花针织物的复杂纹理在疵点检测过程中易造成检测干扰和疵点误判的问题,提出一种基于非线性扩散和多特征融合的疵点检测方法。采用改进PM模型对提花针织物的花纹和强纹理边缘进行抑制,首先利用梯度差异将疵点图像分为纹理区域及疵点区域,然后结合各区域特点选择对应的扩散方程,依据梯度矩阵计算概率子集、相关准则来确定梯度阈值,实现分区域扩散。根据提花针织物的纹理分布特性,提取改进局部二值算法(LBP)、局部熵、局部相关性等表征参数,然后进行去邻域归一化和多特征融合进一步突出疵点区域,最后利用区域生长法定位分割出疵点形态。实验验证了本文预处理方法及疵点检测方法的有效性,通过与其它预处理算法和疵点检测算法进行对比,结果表明本文算法的检测效果最好,对正常织物图像的误检率为3.3%,对含疵点织物图像检测的准确率为98.6%。

基于双向椭圆局部二值模式的环境声音分类

针对目前机器学习算法在环境声音分类准确率不高,训练速度慢的问题,提出了基于双向椭圆局部二值模式的环境声音分类方法。设计了双向椭圆局部二进制模式的音频信号特征提取方法,采用3×5信号邻域增加时长影响,并使用邻域左右两列整体平均值分别代替椭圆左右顶点像素,减少噪音干扰,提高对噪音的鲁棒性,使用整个邻域的平均值代替中心像素,并采用双向局部特征均衡顺序权重,在上述特征基础上增加VAR算子,反应局部特征差异强度,之后将这些特征与梅尔频率倒谱系数(MFCC)、伽玛频率倒谱系数(GFCC)和色度特征(Chromagram)融合。采用经典机器学习算法,如支持向量机(SVM)、随机森林(RF)和k近邻(kNN),结合融合特征,在ESC-10和ESC-50数据集上进行评估,两种数据集的分类准确度分别达到了90.9%和66.7%。

结合LBP圆形算子的CNN面部表情识别研究

利用机器学习中卷积神经网络(CNN)擅长处理图像的优势,结合改进的局部二值模式(LBP)圆形算子,实现了人脸面部表情的识别。提取的人脸表情特征纹理信息得到增强,抑制了图像中光照、背景等干扰因素,并达到了灰度和旋转不变性的要求。在FER2013数据库上的实验结果表明,相比于原始图像的输入,结合LBP圆形算子的CNN结构能够有效提高面部表情识别的准确率。