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.

Algorithm of far-field centre estimation based on phase-only matched filter

Estimation of the far-field centre is carried out in beam auto-alignment. In this paper, the features of the far-field of a square beam are presented. Based on these features, a phase-only matched filter is designed, and the algorithm of centre estimation is developed. Using the simulated images with different kinds of noise and the 40 test images that are taken in sequence, the accuracy of this algorithm is estimated. Results show that the error is no more than one pixel for simulated noise images with a 99% probability, and the stability is restricted within one pixel for test images. Using the improved algorithm, the consumed time is reduced to 0.049 s.

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.

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。

Linux下基于Netfilter的包过滤算法

通过对Linux操作系统下Netfilter防火墙中包过滤技术的分析,发现Netfilter包过滤使用简单的线性分级算法,当防火墙需要匹配的规则越来越多时,防火墙的性能会急剧下降,造成系统瓶颈。因此,提出一种基于二叉树和Hash函数的包过滤算法B-H。通过测试证明,该算法在大量规则的情况下能够达到快速匹配,有效地提高了包过滤的性能。

一种适用于雷达侦测的UWB-SOSCA算法

针对宽带雷达检测门限值设置难度大、系统复杂度高、实时性差等问题,提出了一种UWB-SOSCA(Ultra-Wide Band-Smallest of Ordered Statistics and Cell Average)检测算法。该算法主要包括多相滤波、SOSCA恒虚警检测和二元积累。其中,多相滤波将宽带信号划分为多个子信道,降低通道速率;SOSCA恒虚警检测分别对各子信道进行自适应门限调整,提高各子信道检测概率;二元积累对各子信道的检测结果进行时域联合,优化宽带时域连续检测性能。实验结果表明,在信噪比等于7.33 dB,虚警概率为10~(-5)时,SOSCA恒虚警检测算法与传统恒虚警算法相比,检测概率可提升14%;当多相滤波输入信噪比为-5 dB时,UWB-SOSCA算法的宽带线性调频信号脉冲宽度测量偏差优于6.5 ns。该算法具有大监视带宽、大动态范围和高检测概率,可广泛应用于侦察接收机的雷达侦测工作。

DESIGN OF DIGITAL IMPACTING FILTER IN CP-EBPSK WITH RANDOM-POLAR COMMUNICATION SYSTEM

To solve the difficulty of designing digital impacting filter in the receiver of random-polar modulated Extended Binary Phase Shift Keying with Continuous Phase (CP-EBPSK), a design method based on Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is proposed. Firstly, QPSO is introduced elaborately, and the basic flow of QPSO is also given. Then, the demodulation principle of digital impacting filter in the communication system of CP-EBPSK with random-polar is demonstrated, and QPSO is utilized to design the digital impacting filter, which also takes the effect of finite word length into consideration when implemented by hardware. Finally, the proposed method is simulated. Simulation results show that the digital impacting filter designed by new method can derive satisfied demodulation performance.

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.

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.

Throughput-efficient wireless system and blind detection via improved particle filtering

This paper introduces throughput-efficient wireless system based on an extension to binary phasemodulations,named extended binary phase shift keying(EBPSK),and the corresponding analysis ofpower spectra,especially the extension to channel capacity are given.Importantly,a novel sequential es-timation and detection approach for this EBPSK system is proposed.The basic idea is to design a proba-bilistic approximation method for the computation of the maximum a posterior distribution via particle fil-tering method(PF).Subsequently,a new important function in PF is presented,so that the performanceof the detector has a great improvement.Finally,computer simulation illustrates that EBPSK system hasvery high transmission rate,and also the good performance of the proposed PF detector is demonstrated.

ZigBee Signal Phase Tracking Using a Discrete Kalman Filter Estimator under AWGN Channel

The objective of this research is to track the phase changes in Binary Phase Shift Keying (BPSK) modulated signal in ZigBee communication systems using discrete Kalman Filter (KF). Therefore, Kalman Filtering is used to estimate and optimize the carrier phase of BPSK modulated signal, in the presence of Additive White Gaussian Noise (AWGN) channel, by minimizing the phase deviation error. Therefore, a simulation model, using MATLAB, will be created to demonstrate ZigBee transmission system with the impact of the integrated filter. The expected results will show that Kalman Filter tracks the phase of BPSK modulated signal correctly and the performance of tracking will be measured by Mean Square Error (MSE) with respect to Signal to Noise Ratio (SNR). This study proposes a new method of phase tracking in ZigBee receivers in the presence of AWGN channel which can be extended to Internet of Things (IoT) applications.

Prediction-Based Distance Weighted Algorithm for Target Tracking in Binary Sensor Network

Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.

采用精确抠图的单幅自然图像背景虚化合成方法

针对小光圈镜头下的单幅自然图像背景虚化问题,提出一种基于精确抠图的单幅自然图像背景虚化合成方法.首先,利用GrabCut算法对单幅自然图像的前景对象进行初始抠图;其次,采用基于局部二值模式算子的颜色恒常性算法提取图像纹理,并对初始抠图实现精确割图;然后,通过双边滤波虚化背景部分;最后,将精确分割后的前景部分与虚化后的背景部分合成.仿真结果表明:文中算法可以获得高质量的分割和视觉自然的背景虚化效果.

基于混合计数布隆过滤器的高效数据名查找方法

数据名查找是信息中心网络、内容分发网络、5G核心网中基础功能组件的关键操作,需要面向大规模规则表进行最长前缀匹配,在查找速度、更新开销和存储开销等方面面临严峻挑战.首先设计了混合计数布隆过滤器(HyCBF),将数据名前缀和前缀标记维护在同一个计数布隆过滤器中同时保持二者的逻辑独立性.这样可在不增加额外存储开销和时间开销的情况下提供更丰富的指示信息.基于此,提出HyCBF辅助的二分数据名查找(HyBS)方法以实现高效查找.进一步,为缓解二分查找过程中因回溯导致的性能损失,为HyCBF中每个条目关联一个特征比特位图以降低其假阳性率.实验表明,HyBS相比现有方法在查找性能和更新速度方面具有明显优势,存储效率也有一定提升.此外,将HyBS集成到向量化数据包处理(VPP)框架中进行系统性能评估,结果表明HyBS可用于构建高通量可扩展的数据名查找引擎.

激光雷达信号检测系统的最佳匹配滤波方法

为了解决激光雷达回波信号弱、干扰强问题,提出激光雷达信号检测系统的最佳匹配滤波方法。通过二进小波变换与模极大值分解激光雷达信号检测系统采集回波信号,获取小波系数,保留回波信号特征的同时增加信号输出信噪比,采用匹配滤波处理所获得的小波系数,并经阈值处理后获取小波系数值估计值,利用小波重构估计值得出噪声滤除后信号。实验结果表明:该方法滤波处理后的信号波形均匀、波动幅度小,在不同的尺度因子下,本方法的噪声滤除结果均在80%以上,在固定的尺度因子和噪声输入时,本方法的噪声滤除了82%,检测精度为97.4%,耗时为10.34 ms,均优于其他方法。

融合项目热门惩罚因子改进协同过滤推荐方法

推荐系统是大数据时代解决信息过载问题的一种重要工具,协同过滤是推荐系统中出现最早、应用最广泛的一种推荐算法。针对传统协同过滤推荐算法存在的项目热门度偏差问题,提出了一种融合项目热门惩罚因子改进协同过滤推荐方法。引入热门阈值,根据项目热门度将项目进行二分类,即热门项目(项目热门度较高的项目)和非热门项目(项目热门度较低的项目)。重点针对热门项目,融合项目热门惩罚因子改进协同过滤推荐方法,降低热门项目的贡献,从而提升推荐精度。在MovieLens 100K数据集上对所提推荐方法进行实验验证。实验结果表明,在参数取最优值时,所提推荐方法较为有效地降低了评分预测的平均绝对误差和均方根误差,一定程度上验证了项目热门惩罚因子的有效性。

LEO卫星通信中的两阶段伪码捕获算法

针对低轨卫星通信中高动态和低信噪比引起的捕获概率低、频偏估计误差大的问题,提出两阶段伪码捕获算法.仿真分析结果表明:在低信噪比场景下,相对于PMF-FFT(partial matched filter-fast Fourier transform)伪码捕获算法,两阶段伪码捕获算法有更强的抗噪声性能、更高的捕获概率;相对于PMF-FFT和差分PMF-FFT伪码捕获算法,两阶段伪码捕获算法有最高的频偏估计精度.

降低FBMC-OQAM系统中PAPR的GA-IBPSO算法

针对目前采用偏移正交幅度调制方式下的滤波器组多载波(Filter Bank Multi-Carrier Offset Quadrature Amplitude Modulation, FBMC-OQAM)技术具有峰均比(Peak-to-Average Power Ratio, PAPR)较高的缺点,设计了一种改进的离散二进制粒子群优化的遗传算法(Genetic Algorithm-Improved Binary Particle Swarm Optimization, GA-IBPSO).它选用带外衰减性能更好的原型滤波器代替原有的PHYDAYS来提高抑制效果,同时采用双层部分传输序列(Bilayer Partial Transfer Sequence, BPTS)的思想将临时存储的单层相位因子搜索结构转换成双层结构以扩大搜索范围,并融入遗传算法(Genetic Algorithm, GA)中的交叉、变异操作来进一步降低所得到的次优相位因子序列的计算复杂度.通过理论分析和仿真实验发现,相对于BPSO算法,GA-IBPSO算法通过计算量的略微增加获得了更好的PAPR抑制性能.

二元复合滤棒压降稳定性的影响量研究

为提高二元复合滤嘴棒的质量稳定性,本文对二元复合滤棒的压降稳定性进行了分析,得出影响压降稳定性的原因主要是白棒和料棒的压降波动性大。在对白棒和料棒压降的主要影响量进行的定性、定量统计分析基础上,进行了参数调整和设备改进,使得二元复合滤棒的压降标准偏差由106.56Pa降低到了79.23Pa,在很大程度上降低了基棒压降的波动性,使二元复合滤棒的压降稳定性有了明显提升。

基于正交法和经验法提升二元复合滤棒吸阻稳定性的研究

针对二元复合滤棒的质量稳定性问题,文中对二元复合滤棒的吸阻稳定性进行了定性和定量分析,得出影响吸阻稳定性的原因主要是两种基棒即白棒和料棒的吸阻波动性大;分析了白棒和料棒吸阻的主要影响量;利用正交法进行了白棒开松参数优化,利用经验法对料棒设备进行了改进。实验表明,该措施使二元复合滤棒的吸阻稳定性得到有效改善,并在实际生产中得到良好应用。