Binary Segmentation Method Research Articles

Enhancing sensitivity in fluorescence measurements across an ultra-wide concentration range through optimizing combined-segments strategy

Fluorescence measurement technology is crucial in analytical instrumentation. However, when applied in fields such as environmental science and medical research for quantification purposes, it requires prior estimation of sample concentration and sample dilution to ensure measurement precision. This requirement presents challenge for its broader application in online fluorescence sensing. This study addresses the challenge of achieving high-precision measurements across an ultra-wide concentration range, which is critical for applications ranging from environmental monitoring to clinical diagnostics. The binary segmentation method can initially achieve the goal of ultra-wide range fluorescence measurement, but it fails to maintain sensitivity and precision across the entire range, with relative errors exceeding ±15%. Therefore, we propose an optimizing combined-segments strategy, which examines the effects of adjusting the fluorescence reception position, range, and region interval parameters of the fluorescence reception probes on measurement sensitivity limits. This strategy aims to find the optimal measurement sensitivity limit (lmopt) and the best combined-segments configuration based on more than one quantitative relationship curves. Experimental results demonstrate that within a concentration range 20 times broader than the linear range, it can effectively maintain relative errors within ±5%. This enhances the applicability of fluorescence sensing techniques in various practical settings and advances online and direct fluorescence measurement technologies across an ultra-wide concentration range.

A novel framework for concealed weapons detection using passive terahertz imaging

ABSTRACT In this work, a novel framework for non-destructive, non-intrusive and completely automatic concealed weapon detection under human clothing for entry control and security check applications using passive terahertz (THz) images has been given. The technique models the problem of concealed weapon detection as that of binary segmentation, where the pixels corresponding to concealed weapons are foreground while the remaining are background. The core idea of the proposed framework is to first oversegment the THz image into superpixels and subsequently multiple handcrafted features are extracted from each superpixel. Finally, a machine learning-based classifier is used to perform binary classification thereby classifying each superpixel as foreground or background such that each pixel in a foreground superpixel is a foreground pixel and vice-versa for background. It is worth mentioning that this technique does not require a perfect segmentation to extract features from, rather an oversegmentation suffices. Furthermore, both global features like those based on image saliency and local features like those based on intensity are extracted considering the image properties. The detailed experiments and comparative analysis confirm that the proposed technique efficiently detects concealed weapons, exhibiting superior performance compared to state-of-the-art binary segmentation methods for the purpose.

Application of change-point analysis to HPV infection and cervical cancer incidence in Xinjiang, China in 2011–2019

ObjectiveCervical cancer (CC), serving as a primary public health challenge, significantly threatens women’s health. However, in terms of change-points, there is still a lack of epidemiological studies on the incidence of HPV infection and CC in Xinjiang,China. This research aims to identify significant changes in the trends of HPV infection and CC prevalence in Xinjiang through change-point analysis (CPA) to provide scientific guidance to health authorities.MethodsHPV infection and CC time-series data (from January 2011 to December 2019) were collected and analyzed. Meanwhile, their change-points were detected with binary segmentation method and the PELT method. Furthermore, patients were assigned into three groups based on their different ages and subsequently subjected to an analysis employing a segmented regression model (SRM).ResultsIt was evident that for the monthly HPV time series, the binary segmentation method detected three change points in August 2015, February 2016, and September 2017 (with the most HPV cases). In contrast, the PELT method detected two change-points in September 2015 and April 2017 (with the most HPV cases). For the monthly CC time series, the binary segmentation method identified two change points in October 2012 and August 2019 (with the most CC cases), whereas the PELT method identified three change points in October 2012, August 2019 (with the most CC cases), and October 2019. The SRM demonstrated varying numbers of change points in distinct groups, with HPV infection and CC having the higher growth rate in the 30–49 and 40–59 age groups, respectively. Based on above results, this research was conductive to comprehending the epidemiology of HPV infection and CC in Xinjiang. In addition, it offered scientific guidance for future prevention and management measures for both HPV infection and CC.

Genetic Clustering Algorithm Based on the Division and Combination of Layer Series of Development

Petroleum is a critical industrial material. In the process of oilfield development, the Fisher’s optimal segmentation method is often used to solve the problem of division and combination of layer series of development. However, when encountering the problem of long samples, this method has particularly obvious drawbacks due to the high storage requirements of the calculation process. Therefore, in practical work, the Fisher’s optimal binary segmentation method is generally used instead. Although it avoids storage problems, it is prone to falling into local optima. On the basis of analyzing the shortcomings of Fisher’s optimal segmentation and optimal binary segmentation algorithms, this paper processes a genetic clustering algorithm. This algorithm overcomes the problem of Fisher’s optimal binary segmentation algorithm easily falling into local optima and solves the problem of high storage capacity requirements in the calculation process of Fisher’s optimal segmentation algorithm. Taking the data of 17 subzones in S-2 8-11 sand groups of the Shahejie Formation of the Lower Tertiary in the Dongxin area as an example, this algorithm is applied to divide and combine layer series of development. The experimental results show that the algorithm’s partitioning results are reasonable and can optimize the selection of development layers and provide decision support for the production of oil and gas resources.

PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called PARSEG, which stands for PArtitioning, Random Selection, Estimation, and Generalization; being the basic steps within this procedure. Suggested method enables us to perform statistical validation of binary images by selecting the minimum number of pixels from the original image to be used for validation without deteriorating the effectiveness of the validation procedure. It utilizes binary classifiers to accomplish image validation and selects the optimal sample of pixels according to a specific objective function. As a result, the computational complexity of the validation experiment is substantially reduced. The procedure’s effectiveness is illustrated by considering images composed of approximately 13 million pixels from the field of seed recognition. PARSEG provides roughly the same precision of the validation process when extended to the entire image, but it utilizes only about 4% of the original number of pixels, thus reducing, by about 90%, the computing time required to validate a binary segmented image.

Hepatitis B time series in Xinjiang, China (2006-2021): change point detection based on the Mann-Kendall-Sneyers test.

Hepatitis B is a major global challenge, but there is a lack of epidemiological research on hepatitis B incidence from a change point perspective. This study aimed to fill this gap by identifying significant change points and trends in hepatitis time series in Xinjiang, China. The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. The Mann-Kendall-Sneyers (MKS) test was used to detect change points and trend changes on the hepatitis B time series of 14 regions in Xinjiang, and the effectiveness of this method was validated by comparing it with the binary segmentation (BS) and segment regression (SR) methods. Based on the results of change point analysis, the prevention and control policies and measures of hepatitis in Xinjiang were discussed. The results showed that 8 regions (57.1%) with at least one change fell within the 95% confidence interval (CI) in all 14 regions by the MKS test, where five regions (Turpan (TP), Hami (HM), Bayingolin (BG), Kyzylsu Kirgiz (KK), Altai (AT)) were identified at one change point, two change points existed for two regions (Aksu (AK), Hotan (HT)) and three change points was detected in 1 region (Bortala (BT)). Most of the change points occurred at both ends of the sequence. More change points indicated an upward trend in the front half of the sequence, while in the latter half, many change points indicated a downward trend prominently. Finally, in comparing the results of the three change point tests, the MKS test showed a 61.5% agreement (8/13) with the BS and SR.

SURVS: A Swin-Unet and game theory-based unsupervised segmentation method for retinal vessel

Medical images, especially intricate vascular structures, are costly and time-consuming to annotate manually. It is beneficial to investigate an unsupervised method for vessel segmentation, one that circumvents the manual annotation yet remains valuable for disease detection. In this study, we design an unsupervised retinal vessel segmentation model based on the Swin-Unet framework and game theory. First, we construct two extreme pseudo-mapping functions by changing the contrast of the images and obtain their corresponding pseudo-masks based the on binary segmentation method and mathematical morphology, then we prove that there exists a mapping function between pseudo-mappings such that its corresponding mask is closest to the ground true mask. To acquire the best-predicted mask, based on which, we second develop a model based on the Swin-Unet frame to solve the optimal mapping function, and introduce an Image Colorization proxy task to assist the learning of pixel-level feature representations. Third, since to the instability of two pseudo-masks, the predicted mask will inevitably have errors, inspired by the two-player, non-zero-sum, non-cooperative Neighbor's Collision game in game theory, a game filter is proposed in this paper to reduce the errors in the final predicted mask. Finally, we verify the effectiveness of the presented unsupervised retinal vessel segmentation model on DRIVE, STARE and CHASE_DB1 datasets, and extensive experiments show that has obvious advantages over image segmentation and conventional unsupervised models.

Research on internal defect detection of epoxy glass fiber composites based on terahertz imaging technology

Composite materials are characterized by light weight and high specific strength, which have been widely used in the field of transportation. The rapid and non-destructive detection of defects in the process of composite material production can be regarded as the key and guarantee for the safety. In this paper, the terahertz imaging technology was used to explore the detection of epoxy glass fiber composite samples with varied defects. Terahertz time-domain spectroscopy (THz-TDS) of different depths of defects in epoxy glass fiber samples were corrected by multiple pretreatment methods. Different variable selection methods were used to screen the spectral data. The Partial Least Squares (PLS) and Least Squares Support Vector Machines (LS-SVM) defect depth quantitative models were established, respectively. The visualization of the defect area is achieved by combining with different types of binary threshold segmentation methods. It was found that the ALS-SPA-LS-SVM model presents the best effects, and Rp and RMSEP of prediction set are 0.9998 and 0.0152 respectively. The grayscale histogram bimodal method has the best effect on detecting the defect area. It is feasible to detect the defects in epoxy glass fiber by THz-TDS combined with LS-SVM and image processing technology, which can achieve rapid and nondestructive detection of the location, depth and area of the defects. It provides the technical basis for defect detection in the process of composite material production. In this way, this study provides new methods and means for defect detection and quality supervision of other types of composite materials.

Detection and Estimation of Change Point in Volatility Function of Foreign Exchange Rate Returns

This work aims at detection and estimation of a change point in conditional variance function of a Nonparametric Auto-Regressive Conditional Heteroscedastic model. The conditional mean and conditional variance functions are not specified a priori but estimated using Nadaraya Watson kernel. This is because inferences based on nonparametric approaches are robust against misspecification of the conditional mean function and the conditional variance function of returns. The squared residuals obtained after estimating the regression function of the returns are used in estimating the conditional variance function. Further, the squared residuals are used in developing a test statistic for unknown abrupt change point in volatility of the exchange rate returns. The test statistic takes into consideration the conditional heteroskedasticity of the disturbances, dependence of the returns, heterogeneity and fourth moment of returns. This does not require prior knowledge of the marginal or the conditional densities of the returns as opposed to maximum likelihood estimation methods. The estimator for change point is considered as the augmented maximum of the test statistic. The consistency of the estimator is stated as a theorem. The asymptotic distribution associated with the test for unknown break points is the Bessel process distribution. The Bessel process distributions have no known simple closed-form expression for the distribution function which makes it difficult to compute exact p-values. Also, the Bessel process distributions depend on two parameters which makes it hard to tabulate the critical values hence one needs to simulate them. After simulating the critical values, hypothesis testing is done in the presence and absence of a change point in volatility of a simulated time series and the test is shown to reject the null hypothesis in the presence of a change point at alpha level of significance. Further, the test fails to reject the null hypothesis in the absence of a change point at alpha level of significance. An application to United States Dollar/Kenya Shilling historical exchange rates returns is made from 1^st January 2010 to 27^th November 2020 where the sample size n = 2839 is done. Through binary segmentation method, three change points are detected, estimated and accounted for. A significant improvement in describing a time series is expected if a point in time for volatility change has been detected and estimated.

Optimal Change-Point Analysis of Pavement Condition Data for Identification of Homogeneous Sections

This paper examined the applicability of the pruned exact linear time (PELT) algorithm for optimal change-point analysis to delineate pavement into homogeneous sections based on road condition. The primary objective of the study was to address some of the limitations associated with existing delineation approaches, such as the AASHTO cumulative difference approach (CDA) and the Bayesian binary segmentation method. The paper presents a mathematical framework for the optimal change-point analysis of pavement deflection data collected on a runway of an international airport. Through an analytical comparative study, the paper demonstrated how the proposed approach is better than existing delineation methods in terms of accuracy in identifying the change points, flexibility in the choice of the number of homogeneous sections, and computational time. The approach provides an opportunity for practitioners and road agencies to integrate their experience and knowledge of the road network into decision-making about the location of change points and their count without compromising computational accuracy. The proposed method was found to be robust because the solutions are independent of the parameters chosen for the analysis.

Impact of COVID-19 on public social life and mental health: a statistical study of google trends data from the USA

The COVID-19 pandemic has caused a significant disruption in the social lives and mental health of people across the world. This study aims to asses the effect of using internet search volume data. We categorize the widely searched keywords on the internet in several categories, which are relevant in analyzing the public mental health status. Corresponding to each category of keywords, we conduct an appropriate statistical analysis to identify significant changes in the search pattern during the course of the pandemic. Binary segmentation method of changepoint detection, along with the combination of ARMA-GARCH models are utilized in this analysis. It helps us detect how people's behavior changed in phases and whether the severity of the pandemic brought forth those shifts in behaviors. Interestingly, we find that rather than the severity of the outbreak, the long duration of the pandemic has affected the public health status more. The phases, however, align well with the so-called COVID-19 waves and are consistent for different aspects of social and mental health. We further observe that the results are typically similar for different states as well.

İNSANSIZ HAVA ARAÇLARI İÇİN U-NET TABANLI ARAÇ TESPİT YÖNTEMİ

With the developments in computer hardware technology, studies in the fields of computer vision and artificial intelligence has accelerated. However, the number of areas where autonomous systems are used has also increased. Among these areas are unmanned aerial vehicles, which are one of the most important parameters of today's military technology. In this study, which includes two different scenarios, we aimed to improve the vision capabilities of unmanned aerial vehicles based on artificial intelligence. Within the scope of Scenario-1, the U-Net model suitable for binary semantic segmentation method was trained with the help of images taken by unmanned aerial vehicle camera. Within the scope of Scenario-2, which is designed for moving or stationary vehicle detection, the U-Net model is trained in accordance with multi-class semantic segmentation method. In all these training processes, a publicly available dataset was used. The model trained for Scenario-1 reached mean Intersection over Union (mIoU) value of 84.3%, while the model trained for Scenario-2 reached 79.7% mIoU. In this study, approaches were shared about the use of high-resolution images in model training and testing stages. Applying such studies in the field can help improve precision and reliability in arms industry.

Research on Vehicle Detection and Recognition Based on Infrared Image and Feature Extraction

Vehicle detection and identification and safe distance keeping technology have become the main content of current intelligent transportation system research. Among them, vehicle detection and recognition is one of the most important research contents, and it is also crucial to the safe driving of vehicles. Real-time detection and recognition of current vehicles can effectively prevent the occurrence of malignant traffic accidents such as rear-end collision. Because the infrared image has some shortcomings such as poor contrast, loud noise, and blurred edge, this paper mainly studies the color space preprocessing of the image and uses threshold segmentation method and infrared image enhancement to segment the front vehicle and background. That is to say, by analyzing the infrared image captured by infrared CCD, we use median filter to remove noise from the collected infrared image and then use the improved histogram equalization to enhance the contrast of the image. Vertical Sobel operator is selected to enhance the vertical edge of the image, and the image is segmented by binary segmentation method. Finally, vehicle detection and recognition are realized by vertical edge symmetry, aspect ratio, and gray-scale symmetry. The experimental image and experimental data analysis results show that the image processing technology studied in this paper has achieved the intended research purpose.

How to identify the different phases of stock market bubbles statistically?

Eugene Fama once mentioned in 2016 that people have not come up with ways of identifying bubbles statistically. This paper presents the nonparametric change-point method to identify different stages of stock bubbles, and we derive its asymptotic distribution under the null hypothesis. By simulation, we obtain the corresponding critical value. In the empirical analysis, we employ this test and binary segmentation method to the 1990s Nasdaq bubble and get the same result as Phillips et al. (2011). We also apply this test to the S&P 500 index, the Shanghai stock index, the Nikkei 225 index, the FTSE 100 index, and the CAC 40 index respectively, and successfully identify the bubbles’ different phases in each stock market.

Ensemble binary segmentation for irregularly spaced data with change-points

We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the ensemble binary segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the binary segmentation method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying autoregressive conditional duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package eNchange, available to download from CRAN.

Data-driven quantification of public–private partnership experience levels under uncertainty with Bayesian hierarchical model

Public–private partnership (PPP) is increasingly encouraged to deliver public services in developing countries. Many studies have been conducted to identify factors that affect PPP contract failure. Although a country’s PPP experience is of great importance in controlling the contract failure rate, most of the current studies are based on a qualitative perspective. This research develops a data-driven approach to quantify countries’ PPP experience levels through the Bayesian hierarchical model with uncertainties considered. First, detailed data exploration and selection have been carried out to clean the data source. Second, the number of change points in the dataset is identified based on the binary segmentation method. Third, the Bayesian hierarchical model is developed to locate the positions of the change points, and different experience levels are divided based on the location of change points. Findings show that: (i) PPP experience level is widely varying depending on PPP sectors. Four experience levels are suggested for the energy sector, while five levels are found for the transportation sector, and water & sewerage sector, (ii) PPP experience level is dispersed around the world, for example, Latin America and Caribbean (LAC) and East Asia and Pacific (EAP) regions have higher PPP experience levels than other regions, (iii) a country may have various experience levels in different sectors, such as India, and (iv) the learning rate will decreases as more PPP projects are initiated. This research can contribute to (a) a novel approach that could detect the change points in PPP project experience, and (b) support investors in the decision making process, such as selecting the most appropriate investment direction, contributing to the development of PPP projects in developing countries.

Segmenting geochemical records using hierarchical probabilistic models

Geochemical records of drill cores can be complex and multi-variate. We address the problem of automatically classifying collections of sequential geochemical data. Our approach uses a change-point analysis based on a probabilistic explanation of the binary segmentation method. Change-points, which separate geochemical data into segments of self-similar trends, are accepted only if they increase the global probability of the data set. Increased model complexity is penalised by an Occam factor that offsets improvements in explaining the data trends. Therefore, simpler models are preferred. The method is insensitive to unequally spaced samples in the geochemical records, negating the need to interpolate data for missing values. Probability analysis requires an estimation of variance in the measured data which affects our ability to detect change-points. Extension of change-point analysis to multiple variables in geochemical data sets is straightforward. Typical classification schemes only segment geochemical data into regions that are explained by constant models: we extend our analysis to include models where chemical concentrations change linearly with depth. The recursive application of the binary segmentation algorithm generates hierarchical models that can be used to classify geochemical records over a variety of distance scales.We provide a clear explanation of the development of the mathematics of change-point analysis with binary segmentation and demonstrate its utility with a suite of examples. We compare our results to published classification methods, and provide two examples of classification on data sets from peat bogs and drill core. In one example we detect linear trends in concentrations of Ti, Cr, and Zr that would not be detected with other methods.

Accelerating Automated Stomata Analysis Through Simplified Sample Collection and Imaging Techniques.

Digital image processing is commonly used in plant health and growth analysis, aiming to improve research efficiency and repeatability. One focus is analysing the morphology of stomata, with the aim to better understand the regulation of gas exchange, its link to photosynthesis and water use and how they are influenced by climatic conditions. Despite the key role played by these cells, their microscopic analysis is largely manual, requiring intricate sample collection, laborious microscope application and the manual operation of a graphical user interface to identify and measure stomata. This research proposes a simple, end-to-end solution which enables automatic analysis of stomata by introducing key changes to imaging techniques, stomata detection as well as stomatal pore area calculation. An optimal procedure was developed for sample collection and imaging by investigating the suitability of using an automatic microscope slide scanner to image nail polish imprints. The use of the slide scanner allows the rapid collection of high-quality images from entire samples with minimal manual effort. A convolutional neural network was used to automatically detect stomata in the input image, achieving average precision, recall and F-score values of 0.79, 0.85, and 0.82 across four plant species. A novel binary segmentation and stomatal cross section analysis method is developed to estimate the pore boundary and calculate the associated area. The pore estimation algorithm correctly identifies stomata pores 73.72% of the time. Ultimately, this research presents a fast and simplified method of stomatal assay generation requiring minimal human intervention, enhancing the speed of acquiring plant health information.

Analysis of changes in hydrological cycle of a pristine mountain catchment. 2. Isotopic data, trend and attribution analyses

Abstract δ18O in precipitation at station Liptovský Mikuláš (about 8.5 km south from the outlet of the Jalovecký Creek catchment) remains constantly higher since 2014 that might be related to greater evaporation in the region of origin of the air masses bringing precipitation to the studied part of central Europe. Increased δ18O values are reflected also in the Jalovecký Creek catchment runoff. Seasonality of δ18O in the Jalovecký Creek became less pronounced since 2014. The most significant trends found in annual hydrological data series from the catchment in the study period 1989–2018 have the correlation coefficients 0.4 to 0.7. These trends are found in the number of flow reversals (change from increasing to decreasing discharge and vice versa), June low flow, number of simple runoff events in summer months (June to September) and the flashiness index. The attribution analysis suggests that drivers responsible for the changes in these data series include the number of periods with precipitation six and more days long, total precipitation amount in February to June, number of days with precipitation in June to September and total precipitation in May on days with daily totals 10 mm and more, respectively. The coefficients of determination show that linear regressions between the drivers and supposedly changed data series explain only about 31% to 36% of the variability. Most of the change points detected in the time series by the Wild Binary Segmentation method occur in the second and third decades of the study period. Both hydrometric and isotopic data indicate that hydrological cycle in the catchment after 2014 became different than before.

Multivariate analysis of variance and change points estimation for high‐dimensional longitudinal data

AbstractThis article considers the problem of testing temporal homogeneity of p‐dimensional population mean vectors from repeated measurements on n subjects over T times. To cope with the challenges brought about by high‐dimensional longitudinal data, we propose methodology that takes into account not only the “large p, large T, and small n” situation but also the complex temporospatial dependence. We consider both the multivariate analysis of variance problem and the change point problem. The asymptotic distributions of the proposed test statistics are established under mild conditions. In the change point setting, when the null hypothesis of temporal homogeneity is rejected, we further propose a binary segmentation method and show that it is consistent with a rate that explicitly depends on p,T, and n. Simulation studies and an application to fMRI data are provided to demonstrate the performance and applicability of the proposed methods.