An Improved Double-Threshold Method Based on Gradient Histogram

This paper analyzes the characteristics of the output gradient histogram and shortages of several traditional automatic threshold methods in order to segment the gradient image better. Then an improved double-threshold method is proposed, which is combined with the method of maximum classes variance, estimating-area method and double-threshold method. This method can automatically select two different thresholds to segment gradient images. The computer simulation is performed on the traditional methods and this algorithm and proves that this method can get satisfying result. Key words gradient histogram image - threshold selection - double-threshold method - maximum classes variance method CLC number TP 391. 41 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and the Project of Chenguang Plan in Wuhan (985003062)Biography: YANG Shen (1977-), female, Ph. D. candidate, research direction: multimedia information processing and network technology.

A New Regularized Minimum Error Thresholding Method

To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of probability distribution,one proposes the regularized minimum error threshold method and treats the traditional minimum error threshold method as its special case.Then one constructs the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution.The impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method is investigated.To verify the effectiveness of the proposed regularized minimum error threshold method,one selects typical grey-scale images and performs segmentation tests.The segmentation results obtained by the regularized minimum error threshold method are compared with those obtained with the traditional minimum error threshold method.The segmentation results and their analysis show that the regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method.It does not exert much impact on object acquisition in case of the addition of a certain noise to an image.Therefore,the method can meet the requirements for extracting a real object in the noisy environment.

Dual threshold search method for asperity boundary determination based on geodetic and seismic catalog data

As an important model for explaining the seismic rupture mode,the asperity model plays an important role in studying the stress accumulation of faults and the location of earthquake initiation.Taking Qilian-Haiyuan fault as an example,this paper combines geodetic method and b-value method to propose a multi-source observation data fusion detection method that accurately determines the asperity boundary named dual threshold search method.The method is based on the criterion that the b-value asperity boundary should be most consistent with the slip deficit rate asperity boundary.Then the optimal threshold combination of slip deficit rate and b-value is obtained through threshold search,which can be used to determine the boundary of the asperity.Based on this method,the study finds that there are four potential asperities on the Qilian-Haiyuan fault:two asperities(A1 and A2)are on the Tuolaishan segment and the other two asperities(B and C)are on Lenglongling segment and Jinqianghe segment,respectively.Among them,the lengths of asperities A1 and A2 on Tuolaishan segment are 17.0 km and 64.8 km,respectively.And the lower boundaries are 5.5 km and 15.5 km,respectively;The length of asperity B on Lenglongling segment is 70.7 km,and the lower boundary is 10.2 km.The length of asperity C on Jinqianghe segment is 42.3 km,and the lower boundary is 8.3 km.

Mammogram Images Thresholding for Breast Cancer Detection Using Different Thresholding Methods

The purpose of this study is to apply different thresholding in mammogram images, and then we will determine which technique is the best in thresholding (extraction) malignant and benign tumors from the rest breast tissues. The used technique is Otsu method, because it is one of the most effective methods for most real world views with regard to uniformity and shape measures. Also, we present all the thresholding methods that used the concept of between class variance. We found from the experimental results that all the used thresholding techniques work well in detection normal breast tissues. But in abnormal tissues (breast tumors), we found that only neighborhood valley emphasis method gave best detection of malignant tumors. Also, the results demonstrate that variance and intensity contrast technique is the best in extraction the micro calcifications which represent the first signs of breast cancer.

Extraction of LUCC with different methods and threshold value

The research of land use and land cover (LUCC) is an important aspect in the global change research. The goal of this study is to find methods of extraction of LUCC’s change outlined and change type from remotely sensed data. Take the country of Fengxian in Shanghai as an example, it was supposed two steps to finish extraction of LUCC information: the first step was to use different methods, which is used to outline change areas; the second step include methods of false composing of two-temporal and threshold value. Through combining two methods, a model rule is built and the LUCC product is obtained, four kinds of change type within the study area are given, and the results are obvious. Finally, the results support the application of the high resolution image and tasseled cap composition (greenness and wetness) in the specific regional too.

基于自适应阈值滤波和S-Method的穿墙人体动作识别

穿墙人体动作识别在武装反恐、城市巷战、灾害救援、病人监护等领域具有重要的应用价值。传统的基于短时傅里叶变换(Short-Time Fourier Transform,STFT)的时频分析方法时频分辨率低,不利于后期的分类识别。本文提出了一种基于自适应阈值滤波和S-Method的时频特征增强方法,用于墙后人体动作识别。该方法首先利用自适应阈值滤波消除时频图中的噪声,然后采用S-Method方法聚焦能量,提高时频特征,最后利用K最近邻(KNN)分类器对人体动作进行识别。利用频率步进穿墙雷达获取的实验数据进行方法验证,结果表明:相比于传统的STFT方法,本文所提出的方法对走、跑、坐、跳、招手以及原地踏步等6种典型动作的平均识别准确率更高,可达96.11%。

Neutron-gamma discrimination method based on blind source separation and machine learning

The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20).

Optimal multilevel thresholding based on molecular kinetic theory optimization algorithm and line intercept histogram

Among all segmentation techniques, Otsu thresholding method is widely used. Line intercept histogram based Otsu thresholding method(LIH Otsu method) can be more resistant to Gaussian noise, highly efficient in computing time, and can be easily extended to multilevel thresholding. But when images contain salt-and-pepper noise, LIH Otsu method performs poorly. An improved LIH Otsu method(ILIH Otsu method) is presented, which can be more resistant to Gaussian noise and salt-and-pepper noise. Moreover, it can be easily extended to multilevel thresholding. In order to improve the efficiency, the optimization algorithm based on the kinetic-molecular theory(KMTOA) is used to determine the optimal thresholds. The experimental results show that ILIH Otsu method has stronger anti-noise ability than two-dimensional Otsu thresholding method(2-D Otsu method), LIH Otsu method, K-means clustering algorithm and fuzzy clustering algorithm.

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions:Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

Probabilistic Rainfall Thresholds for Landslide Episodes in the Sierra Norte De Puebla, Mexico

The Sierra Norte de Puebla, Mexico, has a record of hundreds of mass removal processes triggered by rainfall, where the intensity and duration of the rain are the main mechanisms. In order to determine threshold values for precipitation as a cause of a landslide, the prior, marginal and conditional probabilities were calculated. A Bayesian method was used for one-dimensional (precipitation intensity) and two-dimensional (precipitation intensity and duration) analysis. This suggested a high probability of mass movement when the precipitation exceeds 60 mm within ten days. A proposed warning system is based on classes in which the threshold is exceeded.

Three-dimensional simulation method of multipactor in microwave components for high-power space application

Based on the particle-in-cell technology and the secondary electron emission theory, a three-dimensional simulation method for multipactor is presented in this paper. By combining the finite difference time domain method and the panicle tracing method, such an algorithm is self-consistent and accurate since the interaction between electromagnetic fields and particles is properly modeled. In the time domain aspect, the generation of multipactor can be easily visualized, which makes it possible to gain a deeper insight into the physical mechanism of this effect. In addition to the classic secondary electron emission model, the measured practical secondary electron yield is used, which increases the accuracy of the algorithm. In order to validate the method, the impedance transformer and ridge waveguide filter are studied. By analyzing the evolution of the secondaries obtained by our method, multipactor thresholds of these components are estimated, which show good agreement with the experimental results. Furthermore, the most sensitive positions where multipactor occurs are determined from the phase focusing phenomenon, which is very meaningful for multipactor analysis and design.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

Study of denoising method for nonhyperbolic prestack seismic reflection data

Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.

Inference of Median Subjective Threshold in Psychophysical Experiments

We consider the response of a test subject upon a skin area being heated with an electromagnetic wave or a contact surface. When the specifications of the electromagnetic beam are fixed, the stimulus is solely described by the heating duration. The binary response of a subject, escape or no escape, is determined by the stimulus and a subjective threshold that varies among test realizations. We study four methods for inferring the median subjective threshold in psychophysical experiments: 1) sample median, 2) maximum likelihood estimation (MLE) with 2 variables, 3) MLE with 1 variable, and 4) adaptive Bayesian method. While methods 1 - 3 require samples of time to escape measured in the method of limits, method 4 utilizes binary outcomes observed in the method of constant stimuli. We find that a) the adaptive Bayesian method converges and is as efficient as the sample median even when the assumed model distribution is incorrect;b) this robust convergence is lost if we infer the mean instead of the median;c) for the optimal performance in an uncertain situation, it is best to use a wide model distribution;d) the predicted error from the posterior standard deviation is unreliable, dominated by the assumed model distribution.

A novel wavelet method for electric signals analysis in underwater arc welding

Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.

山洪灾害雨量预警指标分析方法评述与展望

山洪灾害预警指标是山洪灾害预报预警的核心,我国现阶段主要采用经验法和水位流量反推法确定雨量预警指标,并逐步推广应用复合预警指标法和动态临界雨量法。在系统阐述国内外常用雨量预警指标分析方法及其特点基础上,针对山洪灾害预警指标分析中需考虑的降雨时空分布变化、高含砂水流、泥石流及其他不确定因素影响问题,提出今后雨量预警指标研究方向。

基于贝叶斯正则化的多源/连续冲击载荷识别及试验研究

针对冲击载荷识别中存在病态矩阵求逆的不适定问题和噪声敏感问题,给出一种增广Tikhonov正则化技术的改进贝叶斯方法。通过引入小波阈值方法解决高噪声水平下多源/连续冲击载荷识别精度不佳的问题,在识别过程中自适应地确定最优正则化参数,并有效地剔除噪声对冲击载荷识别的影响。通过开展飞机壁板结构在不同冲击载荷和信噪比噪声下的数值仿真分析,以相关系数和相对误差作为评价指标,对比讨论了基于L曲线法和广义交叉检验法的Tikhonov正则化方法、贝叶斯正则化方法以及该文方法的识别效果,结果表明该文方法兼顾了曲线的光滑性与峰值识别的准确性,在20 dB高噪声水平连续冲击载荷识别时峰值平均误差不超过14%。开展了典型加筋壁板结构的冲击试验,验证了该文方法对实际工程中典型多源/连续冲击载荷的识别能力,峰值平均误差控制在18%以内,为解决工程应用中的载荷识别问题提供了有效途径。

Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.

星载微波部件微放电阈值的改进多粒子蒙特卡罗计算方法

星载微波部件的微放电效应是导致航天器谐振类设备失谐、噪声电平抬高、输出功率下降,甚至影响通信信道乃至整个微波传输系统彻底失效的瓶颈问题之一。在设计阶段对星载微波部件微放电效应进行精准的评估是减少地面反复试验,避免延误研制周期的重要手段。为了进一步改善现有蒙特卡洛方法的计算准确度问题,文中提出了一种精度更高的计算星载微波部件微放电阈值的蒙特卡罗方法,该方法对参与微放电过程的初始电子进行了动态调整,采用四阶龙格-库塔法推进微波部件中电子的运动轨迹,基于Furman模型描述电子与微波表面相互作用的二次电子发射过程,按照碰撞电子产生的实际二次电子个数及对应能量参与碰撞时刻后的微放电过程,该多粒子-多碰撞过程更加客观、准确地表征了微放电效应发生的物理过程。以平板传输线和同轴传输线为例,文中所提出的方法相对于已有的蒙特卡罗方法计算精度显著提升,同时计算效率优于商用CST的粒子模拟结果。

一种改进的行人航迹推算算法研究

针对行人在室内定位不准确的问题,提出一种改进的行人航迹推算算法。在步数检测阶段,提出一种基于运动分割的三阈值峰值检测法,实现了行人在不同运动状态下步数的精准检测。通过使用改进的Weinberg模型实现步长估算。并提出一种基于主方向假设的航向角修正算法,实现行人的航向角修正。最后综合步数、步长和航向角信息实现室内行人的航迹推算。实验结果表明,改进的行人航迹推算算法在室内有较好的稳定性,在室内的平均定位误差<5%,较传统PDR算法在平均定位误差上降低了9.53%,提高了行人在室内定位的精度。