Accurate Background Research Articles

A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

Due to more or less deviations in the imaging system, there will be noise in the image, which makes the image segmentation inaccurate. To divide a natural image into a more accurate binary image, the target and background of the image are effectively separated to achieve a more effective segmentation result. Therefore, this paper proposes an image segmentation algorithm combining a saliency map and neutrosophic set (NS) theory. First, to overcome the problem of weak edges in the image, we highlight the details and use the guided filter to filter the various channels of the natural image. Then, the initial saliency map is generated. After the weighted superposition of the initial saliency map, the local entropy map and the gray scale map, the final saliency map can be generated using the nonlinear function, and it can effectively highlight the foreground information of the image. Second, the saliency map is transformed to the NS domain and interpreted by three subsets: true (T), indeterminate (I), and false (F). According to NS theory, the indeterminacy is reduced, and the segmentation results are finally obtained by using the method of threshold. Various experiments were done and compared with other state-of-the-art approaches. These experiments demonstrate the effect of the proposed work, which is fast and effective for de-noising.

Camouflage in arid environments: the case of Sahara-Sahel desert rodents

Deserts and semi-deserts, such as the Sahara-Sahel region in North Africa, are exposed environments with restricted vegetation coverage. Due to limited physical surface structures, these open areas provide a promising ecosystem to understand selection for crypsis. Here, we review knowledge on camouflage adaptation in the Sahara-Sahel rodent community, which represents one of the best documented cases of phenotype-environment convergence comprising a marked taxonomic diversity. Through their evolutionary history, several rodent species from the Sahara-Sahel have repeatedly evolved an accurate background matching against visually-guided predators. Top-down selection by predators is therefore assumed to drive the evolution of a generalist, or compromise, camouflage strategy in these rodents. Spanning a large biogeographic extent and surviving repeated climatic shifts, the community faces extreme and heterogeneous selective pressures, allowing formulation of testable ecological hypotheses. Consequently, Sahara-Sahel rodents poses an exceptional system to investigate which adaptations facilitate species persistence in a mosaic of habitats undergoing climatic change. Studies of these widely distributed communities permits general conclusions about the processes driving adaptation and can give insights into how diversity evolves.

The Certification of Standard Reference Material 1979: Powder Diffraction Line Profile Standard for Crystallite Size Analysis.

This rather long-standing project has resulted in a National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) for the analysis of crystallite size from a consideration of powder diffraction line profile broadening. It consists of two zinc oxide powders, one with a crystallite size distribution centered at approximately 15 nm, and a second centered at about 60 nm. These materials display the effects of stacking faults that broaden specific hkl reflections and a slight amount of microstrain broadening. Certification data were collected on the high-resolution powder diffractometer located at beamline 11-BM of the Advanced Photon Source, and on a NIST-built laboratory diffractometer equipped with a Johansson incident beam monochromator and position sensitive detector. Fourier transforms were extracted from the raw data using a modified, two-step profile fitting procedure that addressed the issue of accurate background determination. The mean column lengths, 〈L〉area and 〈L〉vol, were then computed from the Fourier transforms of the specimen contribution for each reflection. Data were also analyzed with fundamental parameters approach refinements using broadening models to yield 〈L〉area and 〈L〉vol values. These values were consistent with the model-independent Fourier transform results; however, small discrepancies were noted for the 〈L〉area values from both machines and both crystallite size ranges. The fundamental parameters approach fits to the laboratory data yielded the certified lattice parameters.

Vehicle Detection and Counting Using Adaptive Background Model Based on Approximate Median Filter and Triangulation Threshold Techniques

Background subtraction method is widely used for vehicle detection. One of the issues in this method is to find a suitable and accurate background model that works in all conditions. Moreover, setting an appropriate threshold value to discriminate between the moving objects and stationary background plays a crucial role in increasing the detection performance. In this paper, an adaptive background model combined with an adaptive threshold method is proposed. It is demonstrated that the proposed method can efficiently differentiate between moving vehicles and background in urban roads under different weather conditions (i.e., normal, rainy, foggy, and snowy). Comparisons between the proposed method and other methods, such as the adaptive local threshold (ALT) and the three frame-differencing methods show the potential of our approach. The experimental results show that the proposed method increases the average recall value by 16.4% and the average precision value by 21.7% in comparison to the ALT method with a negligible increase in the processing time.

U-Th-PbTOTAL dating of REE-phosphate by electron microprobe: Review and progress

Monazite (Mnz) and xenotime (Xnt) are rare earth element (REE) phosphate minerals that occur in a large variety of rocks. They are known to contain very limited amounts of common Pb, and thus, a date can be calculated by simply measuring the total lead- and actinide-contents. Such a measurement can be done at high spatial resolution with an electron probe microanalyser (EPMA), together with a complete mineral analysis. The U-Th-Pbtotal dating method was developed almost 30 years ago, to provide an in-situ age for several (U, Th)-bearing minerals such as uraninite, Mnz, and Xnt. This technique permits identification of events that are not recorded by other minerals, allows dates to be related to structural or geochemical features, and ultimately reinforces the age interpretation. The principal challenge is the accurate measurement of Pb, which is commonly present at the trace level (< 1,000 ppm).This paper reviews the most recent advances in the U-Th-Pbtotal dating technique of Mnz and Xnt, notably in terms of a) accurate background acquisition using the multipoint background technique, and b) improved peak interference correction for trace and REE elements. This paper also discusses a new method of quantitative element mapping, which helps with the identification of compositional domains in Mnz (or Xnt). Finally, a date cannot be interpreted as an age without the proper context, and several key factors in the interpretation are discussed and illustrated with several case studies.

Accurate EELS background subtraction – an adaptable method in MATLAB

• Electron energy loss spectroscopy (EELS) requires background fitting and removal. • Scripts for background subtraction have been written in MATLAB. • The scripts can be applied to core, low, and ultra-low loss EELS. • Statistical information on the goodness-of-fit is given. • Several examples of background subtraction are presented in the main text. Electron energy-loss spectroscopy (EELS) is a technique that can give useful information on elemental composition and bonding environments. However in practice, the complexity of the background contributions, which can arise from multiple sources, can hamper the interpretation of the spectra. As a result, background removal is both an essential and difficult part of EELS analysis, especially during quantification of elemental composition. Typically, a power law is used to fit the background but this is often not suitable for many spectra such as in the low-loss region (< 50 eV) and when there are overlapping EELS edges. In this article, we present a series of scripts written in MATLAB v. R2019b that aims to provide statistical information on the model used to fit the background, allowing the user to determine the accuracy of background subtraction. The scripts were written for background subtraction of vibrational EELS in the ultralow-loss region near the zero-loss peak but can also be applied to other kinds of EEL spectra. The scripts can use a range of models for fitting, provided by the Curve Fitting Toolbox of MATLAB, and the user is able to precisely define the window for fitting as well as for edge integration. We demonstrate the advantages of using these scripts by comparing their background subtraction of example spectra to the most commonly used software, Gatan Microscopy Suite 3. The example spectra include those containing multiple scattering, multiple overlapping peaks, as well as vibrational EELS. Additionally, a comprehensive guide to using the scripts has been included in the Supplementary Information.

Multi-channel whole-head OPM-MEG: Helmet design and a comparison with a conventional system

Magnetoencephalography (MEG) is a powerful technique for functional neuroimaging, offering a non-invasive window on brain electrophysiology. MEG systems have traditionally been based on cryogenic sensors which detect the small extracranial magnetic fields generated by synchronised current in neuronal assemblies, however, such systems have fundamental limitations. In recent years, non-cryogenic quantum-enabled sensors, called optically-pumped magnetometers (OPMs), in combination with novel techniques for accurate background magnetic field control, have promised to lift those restrictions offering an adaptable, motion-robust MEG system, with improved data quality, at reduced cost. However, OPM-MEG remains a nascent technology, and whilst viable systems exist, most employ small numbers of sensors sited above targeted brain regions. Here, building on previous work, we construct a wearable OPM-MEG system with ‘whole-head’ coverage based upon commercially available OPMs, and test its capabilities to measure alpha, beta and gamma oscillations. We design two methods for OPM mounting; a flexible (EEG-like) cap and rigid (additively-manufactured) helmet. Whilst both designs allow for high quality data to be collected, we argue that the rigid helmet offers a more robust option with significant advantages for reconstruction of field data into 3D images of changes in neuronal current. Using repeat measurements in two participants, we show signal detection for our device to be highly robust. Moreover, via application of source-space modelling, we show that, despite having 5 times fewer sensors, our system exhibits comparable performance to an established cryogenic MEG device. While significant challenges still remain, these developments provide further evidence that OPM-MEG is likely to facilitate a step change for functional neuroimaging.

Background model for the high-energy telescope of Insight-HXMT

Accurate background estimation is essential for spectral and temporal analysis in astrophysics. In this work, we construct the in-orbit background model for the High-Energy Telescope (HE) of the Hard X-ray Modulation Telescope (dubbed as Insight-HXMT). Based on the two-year blank sky observations of Insight-HXMT/HE, we first investigate the basic properties of the background and find that both the background spectral shape and intensity have long-term evolution at different geographical sites. The entire earth globe is then divided into small grids, each with a typical area of 5×5 square degrees in geographical coordinate system. For each grid, an empirical function is used to describe the long-term evolution of each channel of the background spectrum; the intensity of the background can be variable and a modification factor is introduced to account for this variability by measuring the contemporary flux of the blind detector. For a given pointing observation, the background model is accomplished by integrating over the grids that are passed by the track of the satellite in each orbit. Such a background model is tested with both the blank sky observations and campaigns for observations of a series of celestial sources. The results show an average systematic error of 1.5% for the background energy spectrum (26–100 keV) under a typical exposure of 8 ks, and <3% for background light curve estimation (30–150 keV). Therefore, the background model introduced in this paper is included in the Insight-HXMT software as a standard part specialized for both spectral and temporal analyses.

Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study.

BackgroundThe detection of infectious diseases through the analysis of free text on electronic health reports (EHRs) can provide prompt and accurate background information for the implementation of preventative measures, such as advertising and monitoring the effectiveness of vaccination campaigns.ObjectiveThe purpose of this paper is to compare machine learning techniques in their application to EHR analysis for disease detection.MethodsThe Pedianet database was used as a data source for a real-world scenario on the identification of cases of varicella. The models’ training and test sets were based on two different Italian regions’ (Veneto and Sicilia) data sets of 7631 patients and 1,230,355 records, and 2347 patients and 569,926 records, respectively, for whom a gold standard of varicella diagnosis was available. Elastic-net regularized generalized linear model (GLMNet), maximum entropy (MAXENT), and LogitBoost (boosting) algorithms were implemented in a supervised environment and 5-fold cross-validated. The document-term matrix generated by the training set involves a dictionary of 1,871,532 tokens. The analysis was conducted on a subset of 29,096 tokens, corresponding to a matrix with no more than a 99% sparsity ratio.ResultsThe highest predictive values were achieved through boosting (positive predicative value [PPV] 63.1, 95% CI 42.7-83.5 and negative predicative value [NPV] 98.8, 95% CI 98.3-99.3). GLMNet delivered superior predictive capability compared to MAXENT (PPV 24.5% and NPV 98.3% vs PPV 11.0% and NPV 98.0%). MAXENT and GLMNet predictions weakly agree with each other (agreement coefficient 1 [AC1]=0.60, 95% CI 0.58-0.62), as well as with LogitBoost (MAXENT: AC1=0.64, 95% CI 0.63-0.66 and GLMNet: AC1=0.53, 95% CI 0.51-0.55).ConclusionsBoosting has demonstrated promising performance in large-scale EHR-based infectious disease identification.

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory.

Evaluation of wind erosion in the Tarim Basin based on parameter localization

Air density strongly constrains the characteristics of an airflow and the resulting flow of sand and therefore, it should not be neglected in sand transport models. Although the potential importance of air density is well known, most studies on threshold velocity, saltation trajectory, and dust emission have been conducted near sea level, at a “normal” atmospheric density. In this study, utilizing the observational data from 12 meteorological stations in the Tarim Basin (TB) from 2009 to 2018, we used air density as the basis to discuss the necessity of model parameter localization. It was found that there was a significant difference in the horizontal dust flux estimated using the normal and local air densities. Using the localized air density, we re-evaluated the temporal and spatial distribution characteristics of the threshold velocity, horizontal dust flux, and wind erosion events in the TB and further quantified their distributions in all directions. The results demonstrated that the threshold velocity gradually increased from east to west and from north to south, with the maximum value occurring in the southwestern TB. The horizontal dust fluxes and wind erosion events could be classified into three types according to the seasonal variation characteristics; however, one of the main distribution characteristics was spring > summer > autumn > winter. Tazhong, Qiemo, and Ruoqiang are sand- and dust-incident frequent zones. Taking Minfeng when passing from Tazhong to Shaya as the line of demarcation, it was found that the horizontal dust fluxes and wind erosion events in the eastern region mostly occurred in the eastern direction, whereas those in the western region primarily occurred in the western direction. The results suggested that it was extremely important and necessary to localize the parameters in sand–dust models, which would also provide a more accurate climate background for the study of sand and dust in the TB.

Improved modeling of β electronic recoils in liquid xenon using LUX calibration data

We report here methods and techniques for creating an improved model that reproduces the scintillation and ionization response of a dual-phase liquid and gaseous xenon time projection chamber. Starting with the recent release of the Noble Element Simulation Technique (NEST v2.0), electronic recoil data from the β decays of 3H and 14C in the Large Underground Xenon (LUX) detector were used to tune the model, in addition to external data sets that allow for extrapolation beyond the LUX data-taking conditions. This paper also presents techniques used for modeling complicated temporal and spatial detector pathologies that can adversely affect data using a simplified model framework. The methods outlined in this report show an example of the robust applications possible with NEST v2.0 framework and how it can be modified to produce a final, detector-specific, electronic recoil model. This example provides the final model for LUX and detector parameters that will used in the new analysis package, the LUX Legacy Analysis Monte Carlo Application (LLAMA), for accurate reproduction of the LUX data. As accurate background reproduction is crucial for the success of rare-event searches, such as dark matter direct detection experiments, the techniques outlined here can be used in other single-phase and dual-phase xenon detectors to assist with accurate ER background reproduction.

Accurate background correction in neutron reflectometry studies of soft condensed matter films in contact with fluid reservoirs.

Neutron reflectometry (NR) is a powerful method for looking at the structures of multilayered thin films, including biomolecules on surfaces, particularly proteins at lipid interfaces. The spatial resolution of the film structure obtained through an NR experiment is limited by the maximum wavevector transfer at which the reflectivity can be measured. This maximum is in turn determined primarily by the scattering background, e.g. from incoherent scattering from a liquid reservoir or inelastic scattering from cell materials. Thus, reduction of scattering background is an important part of improving the spatial resolution attainable in NR measurements. Here, the background field generated by scattering from a thin liquid reservoir on a monochromatic reflectometer is measured and calculated. It is shown that background subtraction utilizing the entire background field improves data modeling and reduces experimental uncertainties associated with localized background subtraction.

Wave‐Equation Migration Velocity Analysis Using Radon‐Domain Common‐Image Gathers

AbstractThe accuracy of the background velocity analysis is critical to image the subsurface structure in seismology. Wave‐equation migration velocity analysis using source‐domain common‐image gathers requires picking the relative moveouts based on semblance analysis. However, the picking process is complex and time‐consuming. To avoid picking and maintain efficiency in generating CIGs, an automatic wave‐equation migration velocity analysis method is proposed utilizing the focusing properties of the Radon‐domain common‐image gathers. Seismic events in the Radon‐domain common‐image gathers appear focused if an accurate migration velocity model is used; otherwise, the resultant events will be unfocused. The objective function is defined in the Radon domain to measure focus of the common‐image gathers automatically. The proposed migration velocity analysis method links the defocusing information to the migration velocity update under the assumption that model perturbations only result in slope shifts in the common‐image gathers. This method provides a reasonably accurate background velocity model for imaging and full‐waveform inversion without requiring an accurate initial velocity model. Tests on synthetic and field data demonstrate the effectiveness of the method. This method is tolerable to both inaccurate initial velocity models and the lack of low‐frequency data.

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet

Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimensional localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a general method to rapidly estimate the background underlying the image of a point source with excellent accuracy, even when point-spread function (PSF) engineering is in use to create complex PSF shapes. We trained BGnet to extract the background from images of various PSFs and show that the identification is accurate for a wide range of different interfering background structures constructed from many spatial frequencies. Furthermore, we demonstrate that the obtained background-corrected PSF images, for both simulated and experimental data, lead to a substantial improvement in localization precision. Finally, we verify that structured background estimation with BGnet results in higher quality of superresolution reconstructions of biological structures.

CVABS: moving object segmentation with common vector approach for videos

Background modelling is a fundamental step for several real‐time computer vision applications that requires security systems and monitoring. An accurate background model helps to detect the activity of moving objects in the video. In this work, the authors have developed a new subspace‐based background‐modelling algorithm using the concept of common vector approach (CVA) with Gram–Schmidt orthogonalisation. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilised as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self‐learning feedback mechanism.

Image domain least-squares migration with a Hessian matrix estimated by non-stationary matching filters

AbstractAssuming that an accurate background velocity is obtained, least-squares migration (LSM) can be used to estimate underground reflectivity. LSM can be implemented in either the data domain or image domain. The data domain LSM (DDLSM) is not very practical because of its huge computational cost and slow convergence rate. The image domain LSM (IDLSM) might be a flexible alternative if estimating the Hessian matrix using a cheap and accurate approach. It has practical potential to analyse convenient Hessian approximation methods because the Hessian matrix is too huge to compute and save. In this paper, the Hessian matrix is approximated with non-stationary matching filters. The filters are calculated to match the conventional migration image to the demigration/remigration image. The two images are linked by the Hessian matrix. An image deblurring problem is solved with the estimated filters for the IDLSM result. The combined sparse and total variation regularisations are used to produce accurate and reasonable inversion results. The numerical experiments based on part of Sigsbee model, Marmousi model and a 2D field data set illustrate that the non-stationary matching filters can give a good approximation for the Hessian matrix, and the results of the image deblurring problem with combined regularisations can provide high-resolution and true-amplitude reflectivity estimations.

MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection

Moving object detection (MOD) in videos is a challenging task. Estimation of accurate background is the key to extracting the foreground from video frames. In this paper, we have proposed a novel compact end-to-end convolutional neural network architecture, motion saliency foreground network (MSFgNet), to estimate the background and to extract the foreground from video frames. Initially, the long streaming video is divided into a number of small video streams (SVS). The proposed network takes the SVS as an input and estimates the background frame for each SVS. Second, the saliency map is extracted using the current video frame and estimated background. Furthermore, a compact encoder-decoder network is proposed to extract the foreground from the estimated saliency maps. The performance of the proposed MSFgNet is tested on three benchmark datasets (CDnet-2014, LASIESTA, and PTIS) for MOD. The computational complexity (handling of number of parameters and execution time) and the performance of the proposed MSFgNet are compared with the existing state-of-the-art methods for MOD in terms of precision, recall, and F-measure. Performance analysis shows that the proposed network is very compact and outperforms the existing state-of-the-art methods for MOD in videos.

A Background-Purification-Based Framework for Anomaly Target Detection in Hyperspectral Imagery

Anomaly target detection for hyperspectral imagery (HSI) is one of the most important techniques, and many anomaly detection algorithms have been developed in recent years. One of the key points in most anomaly detection algorithms is estimating and suppressing the background information. This letter proposes a background-purification-based (BPB) framework considering the role of background estimation and suppression in anomaly detection. The main idea is the acquisition of accurate background pixel set. To prove the validity of the proposed framework, the BPB Reed-Xiaoli detector (BPB-RXD), the BPB kernel Reed-Xiaoli detector (BPB-KRXD), and the BPB orthogonal subspace projection anomaly detector (BPB-OSPAD) are proposed. Both the BPB algorithms focus on accurate background information estimation to improve the performance of the detectors. The experiments implemented on two data sets demonstrate that both BPB algorithms perform better than other state-of-the-art algorithms, including RXD, KRXD, OSP, blocked adaptive computationally efficient outlier nominators (BACON), probabilistic anomaly detector (PAD), collaborative-representation-based detector (CRD), and CRD combined with principal component analysis (PCAroCRD).

Digital skills and profile of each generation: a review

The technological development is changing, involving and conditioning everyday life, making it faster, hyper-connected and immediate. New technologies can have a double outcome: positive, given that they take on the role of facilitator, or negative, when people tend to make excessive and unconscious use of it or technology repeatedly replaces human beings. Today, therefore, new skills are needed for the entire population, the digital skills, both in everyday life and in the labour market. These are transversal skills that allow human beings to choose the right technologies: to research, analyze data and choose reliable information from those that require further study, to check and verify the health of people, to interact with different and distant subjects in the world, to improve school learning, ... The plurality of people that this technological development is involving allows us to analyze the population in terms of generations, from Silent Generation to Alpha Generation. In this article we want to present a literature review within the Google Scholar database in the last 10 years using the terms digital skills and different generation. This preliminary research allows us to analyze the different theories that make for the Digital Skills a definition, dimensions and evaluation model, and the generational profiles in order to have a shared and common theory among the different professionals. Having an accurate background of digital skills and generational profiles favours the inclusion of all people, and the improvement of the quality of life in the creation of psycho-pedagogical projects and in the design of technological products.