Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

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.

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.

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.

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.

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.

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%.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.

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.

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。

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.

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。

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。

结合改进LBP和SRC的高光谱图像分类研究

针对传统局部二值模型(local binary pattern,LBP)提取高光谱图像纹理特征信息量庞大的难题,提出一种基于对称旋转不变等价局部二值模型(symmetrical rotation invariant uniform LBP,SRIULBP)的高光谱图像特征提取方法,以缩减特征维度;针对稀疏表示分类(sparse representation classification,SRC)模型中稀疏字典冗余的缺陷,采用近邻思想,提出最近邻稀疏表示(nearest neighbor SRC,NNSRC)分类方法,实现高光谱图像的高效、高准确度分类。数据实验结合表明,SRIULBP能快速提取图像特征,提出的分类方法不仅在分类精度上优于其他稀疏表示分类算法,并且具有更强的时效性与泛化能力。

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。

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。