Dynamic Background Research Articles

Spatio-Temporal Online Matrix Factorization for Multi-Scale Moving Objects Detection

Detecting moving objects from the video sequences has been treated as a challenging computer vision task, since the problems of dynamic background, multi-scale moving objects and various noise interference impact the corresponding feasibility and efficiency. In this paper, a novel spatio-temporal online matrix factorization (STOMF) method is proposed to detect multi-scale moving objects under dynamic background. To accommodate a wide range of the real noise distractions, we apply a specific mixture of exponential power (MoEP) distributions to the framework of low-rank matrix factorization (LRMF). For the optimization of solution algorithm, a temporal difference motion prior (TDMP) model is proposed, which estimates the motion matrix and calculates the weight matrix. Moreover, a partial spatial motion information (PSMI) post-processing method is further designed to implement multi-scale objects extraction in varieties of complex dynamic scenes, which utilizes partial background and motion information. The superiority of the STOMF method is validated by massive experiments on practical datasets, as compared with state-of-the-art moving objects detection approaches.

Oculomotor and Perceptual Measures of Visual Motion Sensitivity in Patients With Chronic Dizziness Symptoms.

The purpose of this study was to provide empirical data describing the relationship between behavioral responses to dynamic visual motion in adults with chronic dizziness symptoms with and without clinically identifiable peripheral vestibular impairment. Prospective, quasi-experimental study including individuals with chronic dizziness symptoms with identified unilateral peripheral vestibular impairment (n=27), and individuals with chronic dizziness symptoms without identified vestibular impairment (n=26). We measured (a) visual perception of verticality in a dynamic background, (b) postural displacement in a dynamic background, (c) eye movement behaviors in various visual contexts, and (d) self-rating degree of anxiety. Status of peripheral vestibular function was not a significant predictor of behavioral responses to visual motion. The data show that the ability to fixate on a visual target was predictive of postural control in a dynamic visual background. Trial-to-trial variability in verticality responses and degree of self-rated anxiety were also associated with postural control. Apart from vestibular function, oculomotor control is important for maintaining control of whole-body motor responses during exposure to a dynamic visual stimulus. Vertical perception precision-not accuracy-may be more important for understanding real-world consequences of visual motion sensitivity. Traditional diagnostic evaluations focusing exclusively on characterizing the peripheral vestibular system may not provide insight into the behaviors associated with visual motion sensitivity.

Microwave Brightness Temperature (MBT) Background in Bayan Har Block, Qinghai-Tibet Plateau and Its Importance in Searching for Seismic MBT Anomalies

The abnormal behaviors of microwave brightness temperature (MBT) before and after some strong inland earthquakes have been studied for more than 15 years, but the normal features of MBT background in the investigated regions still lack essential attention. This study focused on the extremely seismically active Bayan Har block on the Qinghai-Tibet Plateau in China, and revealed the spatiotemporal variations of monthly mean background and monthly standard deviation (STD) of MBT by using data of 10.65 and 89 GHz from AMSR-2 instrument. In terms of space, the results revealed that the MBT backgrounds at the two frequencies both basically exhibited a negative correlation with regional altitude but were more pronounce at high frequency. They also showed different response characteristics to the properties of soil and vegetation. In terms of time, the low-frequency background exhibited a complex month-to-month variation, with auxiliary data suggesting a joint contribution of surface soil moisture (SSM) and seasonal temperature; while the high-frequency background presented good agreement only with the variation in surface temperature. Meanwhile, the monthly STD of MBT was discovered being affected by SSM at the low-frequency and by snowfall events at the high-frequency. By employing MBT data of 10.65 GHz from AMSR-E and AMSR-2 sensors, the spatiotemporal evolutions of MBT anomalies before, during and after the Ms 7.1 Yushu earthquake on 13 April 2010 and the Ms 7.4 Maduo earthquake on 21 May 2021 were obtained referring to dynamic monthly mean background. A typical strip-shaped positive MBT anomaly just covering the Bayan Har block was found occurring prior to the two earthquakes, and the time series of average MBT anomaly inside the block was analyzed by using multiple datasets. The typical abnormal MBT strip was discriminated being independent of non-seismic factors and regarded as a possible precursor for both earthquakes. This research uncovered the normal features of MBT background and demonstrated the common characteristics of MBT anomalies preceding two strike-slip earthquakes inside the Bayan Har block. It has instructive significance for studying, understanding and searching for seismic MBT anomalies on Qinghai-Tibet Plateau.

Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.

Research on tracking method of moving object under the dynamic background

In order to solve the problem that the traditional moving target tracking algorithm is vulnerable to interference and difficult to accurately track the target for a long time, we first introduce the common target tracking algorithm under the dynamic background, and then we determine the KCF algorithm which has better tracking effect and real-time performance. Then, aiming at the problem of target box offset in the longtime tracking, a tracking algorithm combining Kalman filter algorithm and KCF algorithm is proposed. Finally, the actual effect proves to be effective.

FPGA IMPLEMENTATION OF OBJECT DETECTION IN BACKGROUND MODELING USING GAUSSIAN MIXTURE MODEL

Demand for implementation of image processing algorithm on FPGA is exponentially increases in the present world. Many research works is carried for image processing applications and implementation on FPGA. Nowadays identification, classification, and recognition of an object in the highly sophisticated regions are big challenge. Tracking intended object in the congestion region is very difficult. CCTV is used to detect and track the object in the traffic regions. Sometimes it gives false alarm by capturing shadows due to change in the intensity levels in the dynamic background. In this paper an efficient background circuit is developed to detect the object in the dynamic/static background to avoid false alarm. The real time captured image is processed by the proposed background circuit, and it is implemented on FPGA. It has been observed that the result obtained from this proposed work is good in terms of processing time and area utilization.

A Review of Foreground Segmentation based on Convolutional Neural Networks

Foreground segmentation in dynamic videos is a challenging task for many researchers. Many researchers worked on various methods that were traditionally developed; however, the performance of those state-of-art procedures has not yielded encouraging results. Hence, to obtain efficient results, a deep learning-based neural network model is proposed in this paper. The proposed methodology is based on Convolutional Neural Network (CNN) model incorporated with Visual Geometry Group (VGG) 16 architecture, which is further divided into two sections, namely, Convolutional Neural Network section for feature extraction and Transposed Convolutional Neural Network (TCNN) section for un-sampling feature maps. Then the thresholding technique is employed for effective segmentation of foreground from background in images. The Change Detection (CDNET) 2014 benchmark dataset is used for the experimentation. It consists of 11 categories, and each category contains four to six videos. The baseline, camera jitter, dynamic background, and bad weather are the categories considered for the experimentation. The performance of the proposed model is compared with the state-of-the-art techniques, such as Gaussian Mixture Model (GMM) and Visual Background Extractor (VIBE) for its efficiency in segmenting foreground images.

An improved ViBe-based approach for moving object detection

Moving object detection is a challenging task in the automatic monitoring field, which plays a crucial role in most video-based applications. The visual background extractor (ViBe) algorithm has been widely used to deal with this problem due to its high detection rate and low computational complexity. However, there are some shortcomings in the general ViBe algorithm, such as the ghost area problem and the dynamic background problem. To deal with these problems, an improved ViBe approach is presented in this paper. In the proposed approach, a mode background modeling method is used to accelerate the process of the ghost elimination. For the detection of moving object in dynamic background, a local adaptive threshold and update rate is proposed for the ViBe approach to detect foreground and update background. Furthermore, an improved shadow removal method is presented, which is based on the HSV color space combined with the edge detection method. Finally, some experiments were conducted, and the results show the efficiency and effectiveness of the proposed approach.

Genesis and tectonic magmatic evolution of Carboniferous granites in the Hongliuhecao-Yuejinshan area of Beishan orogenic belt

北山造山带位于中亚造山带南缘，区内广泛发育古亚洲洋-陆演化的岩浆记录，是研究北山造山带构造-岩浆-成矿作用及其动力学背景的重要窗口。红柳河槽-跃进山地区是北山造山带中北部的重要组成部分，区内与成矿有关花岗岩的形成时代、成因及构造背景尚不明确，由此制约了对北山造山带内成岩成矿动力学背景的探讨。本次在系统、详细的野外地质调查、岩石（相）学观察的基础上，结合岩浆岩地球化学、锆石U-Pb年代学和Lu-Hf同位素等证据，深入剖析了北山造山带内红柳河槽-跃进山地区的花岗岩形成时代、类型、源区和动力学背景，取得了以下认识：（1）通过锆石LA-ICP-MS U-Pb定年测得与成矿有关的狼娃山二长花岗岩、红柳河槽正长花岗岩、跃进山北正长花岗岩、跃进山二长花岗岩形成时代分别为327.1±2.4Ma、326.7±2.4Ma、321.3±2.4Ma和309.7±2.2Ma，表明红柳河槽-跃进山地区铜、钨、钼、铅、锌等元素成矿与石炭纪（327.1~309.7Ma）岩浆活动有关；（2）通过花岗岩地球化学研究表明，红柳河槽-跃进山地区的花岗岩均属于钙碱性-高钾钙碱性、准铝质-弱过铝质系列，强烈富集LREE、LILE，亏损HREE、HSFE，具有典型弧岩浆的地球化学特征，花岗岩成因类型均为I型；（3）本次获得狼娃山二长花岗岩和红柳河槽正长花岗岩的ε_Hf（t）值分别为5.90~9.92（平均值7.93）和5.87~10.28（平均值8.11），t_DM2分别为0.703~0.959Ga和0.93~1.03Ga，且具有较低的Nb/Ta比值（5.1~14.2，平均值8.5），与典型新生下地壳来源岩浆（8.3）相似，表明岩浆物质可能源于新生下地壳岩石部分熔融；（4）根据锆石Ti温度计查明北山造山带红柳河槽-跃进山地区狼娃山二长花岗岩、红柳河槽正长花岗岩、跃进山北正长花岗岩、跃进山二长花岗岩的平均结晶温度分别为765℃、765℃、816.4℃、771.5℃，锆石Ce^（Ⅳ）/Ce^（Ⅲ）平均值分别为85.27、108.4、103.6、135.7，表明从石炭纪早期到晚期，岩浆的氧逸度呈逐渐升高的趋势。根据以上研究，结合前人研究成果，初步建立了北山造山带构造-岩浆演化模型如下：石炭纪北山造山带北部红石山洋向南侧公婆泉-明水-旱山复合构造单元（弧）之下发生俯冲，诱发新生下地壳部分熔融形成的壳源岩浆；其侵入到地壳浅部，并最终形成红柳河槽-跃进山花岗岩体。;Beishan orogenic belt is located in the southern margin of the Central Asian Orogenic Belt. Magmatic records of paleo-Asian ocean-continent evolution widely existed in this area, and as a result, it is an important window to study the tectonic magmatic mineralization and dynamic background of Beishan orogenic belt. Hongliuhecao-Yuejinshan area is an important component of the middle-northern part of Beishan orogenic belt, however, the formation age, genesis and tectonic background of granites related to mineralization in the area are not clear, which seriously restricts the discussion of diagenetic and metallogenic dynamic backgrounds in Beishan orogenic belt. Based on systematic and detailed field geological survey and petrographic (facies) observation, combined with the evidence of magmatic rock geochemistry, zircon U-Pb chronology and Lu-Hf isotope, we analyzes the formation age, type, source area and dynamic background of granite in Hongliuhecao-Yuejinshan area in Beishan orogenic belt, from which the following conclusions can be obtained:(1) According to zircon LA-ICP-MS U-Pb dating, the formation ages of Langwashan monzogranite, Hongliuhecao syenogranite, Yuejinshanbei syenogranite and Yuejinshan monzogranite related to mineralization are 327.1±2.4Ma, 326.7±2.4Ma, 321.3±2.4Ma and 309.7±2.2Ma, indicating that the mineralization of copper, tungsten, molybdenum, lead, zinc and other elements is related to the Carboniferous magmatism of 327.1~309.7Ma. (2) The geochemical study of granites shows that the granites in Hongliuhecao-Yuejinshan area are strongly enriched in LREE and LILE, while deficient in HREE and HSFE, which demonstrated that they have the geochemical characteristics of typical arc magma and the I-type granites, and belong to calc alkaline, high-K calc alkaline, quasi aluminous-weak peraluminous series. (3) The ε_Hf(t) values of Langwashan monzogranite and Hongliuhecao syenogranite are 5.90~9.92 (average 7.93) and 5.87~10.28 (average 8.11), respectively; and the t_DM2 values are 0.703~0.959Ga and 0.93~1.03Ga, respectively; furthermore, they have a low Nb/Ta ratio (5.1~14.2, average 8.5), which is similar to the typical magma from the newborn lower crust (8.3). All these charateristics indicate that the magma material may have originated from the partial melting of the newborn lower crust rocks. (4) According to the zircon Ti thermometer, the average crystallization temperatures of Langwashan monzogranite, Hongliuhecao syenogranite, Yuejinshanbei syenogranite and Yuejinshan monzogranite in the studying area are 765℃, 765℃, 816.4℃ and 771.5℃, respectively; while the zircon Ce^(Ⅳ)/Ce^(Ⅲ)average values are 85.27, 108.4, 103.6, 135.7, respectively, indicating that the oxygen fugacity of magma increased gradually from the early to the late Carboniferous. Based on the above analyses, together with previous research results, the tectonic magmatic evolution model of Beishan orogenic belt is preliminarily established:the Hongshishan ocean in the north of the Beishan orogenic belt subducted under the Gongpoquan-Mingshui-Hanshan composite tectonic unit (arc) in Carboniferous, which induced partial melting of the newborn lower crust and formed the magma; this crustal magam intruded into the shallow crust, and finally formed the Hongliuhecao-Yuejinshan granite.

Background Modeling Combined With Multiple Features in the Fourier Domain for Maritime Infrared Target Detection

To detect ship targets in dynamic sea backgrounds, a robust foreground detection method based on background modeling combined with multiple features in the Fourier domain (BMMFF) is proposed. Because the fluctuation of seawater is similar to a sine wave, the amplitude spectrum of the seawater in the Fourier domain has strong energy concentration, and the background model is built in the Fourier domain. The local statistical characteristics can effectively reflect the properties of a specific position, and the target frequency points are extracted by comparing the local statistical characteristic differences between the amplitude spectrum of the test frame and the updated background. Additionally, two different strategies are used to update the background for cases with or without sudden waves in two adjacent test frames. When the target and seawater have different contrast in different scenes, the threshold is set according to the seawater's fluctuation degree, which is classified into three different levels in the training stage. Moreover, the linear correlation feature between the test frame and the updated background and the oscillation feature of the test frame's amplitude spectrum are proposed in the Fourier domain, which can better segment targets in relatively calm and violent sea scenes, respectively. Thus, the two features are combined with the background model with a reasonable strategy to further improve the detection accuracy. The experimental results demonstrate that BMMFF outperforms related comparison algorithms in different challenging sea scenes. The average false alarm rate of BMMFF is reduced by approximately 30% in the case where its average detection rate is similar to that of other algorithms.

Skeleton Keypoints Extraction Method Combined with Object Detection

Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point extraction method combined with object detection, which can focus on the extraction of skeleton keypoints. After a large number of experiments, our model can be combined with object detection for skeleton points extraction, and the detection efficiency is improved.

CASIA-E: A Large Comprehensive Dataset for Gait Recognition.

Gait recognition plays a special role in visual surveillance due to its unique advantage, e.g., long-distance, cross-view and non-cooperative recognition. However, it has not yet been widely applied. One reason for this awkwardness is the lack of a truly big dataset captured in practical outdoor scenarios. Here, the "big" at least means: (1) huge amount of gait videos; (2) sufficient subjects; (3) rich attributes; and (4) spatial and temporal variations. Moreover, most existing large-scale gait datasets are collected indoors, which have few challenges from real scenes, such as the dynamic and complex background clutters, illumination variations, vertical view variations, etc. In this article, we introduce a newly built big outdoor gait dataset, called CASIA-E. It contains more than one thousand people distributed over near one million videos. Each person involves 26 view angles and varied appearances caused by changes of bag carrying, dressing and walking styles. The videos are captured across five months and across three kinds of outdoor scenes. Soft biometric features are also recorded for all subjects including age, gender, height, weight, and nationality. Besides, we report an experimental benchmark and examine some meaningful problems that have not been well studied previously, e.g., the influence of million-level training videos, vertical view angles, walking styles, and the thermal infrared modality. We believe that such a big outdoor dataset and the experimental benchmark will promote the development of gait recognition in both academic research and industrial applications.

지능형 교통 체계를 위한 딥러닝 기반 객체 검출 활용 방법론

Most machine vision solutions for intelligent transport systems begin with the extraction of hand-crafted visual features from traffic scenes. In this study, the category-specific information for detecting specific visual patterns was investigated by replacing machine vision solutions with a standard convolutional neural network (CNN)-based object detector, and three practical applications of the system were developed. First, our system learned important categories, such as pedestrian and vehicles, for traffic monitoring and management. In addition, while collecting related databases, we efficiently performed data augmentation and improved the recognition accuracy of the system for several user-defined events. Further, the displacement of the detected positions between consecutive frames was converted into the real-world distance to compute the physical velocity of a vehicle. Second, we developed a vision-based system for a real-time lane-level traffic congestion measurement. After tracking the detected vehicles, the estimated velocities of vehicles for each lane were averaged. Subsequently, traffic congestion was determined based on the number of detected vehicles and averaged velocity. Third, we presented a context-aware method for background maintenance. To handle dynamic background objects, we utilized a 2D object detector to identify the category-specific background patterns. The key observation was that the detected regions do not belong to a true background. Hence, we developed a new confidence map to update the static background model and exclude pre-learnt background objects for conventional background subtraction methods. In the study, more than eight user-defined events were suggested by the combination of traditional machine vision techniques and deep learning-based object detectors with a substantial number of training images. In addition, our key ideas were validated using various datasets, such as five different scenes for lane-level traffic congestion and two CCTV image sequences for object-aware background subtraction and unseen object detection in the challenging traffic congestion. Lastly, the suggested applications of this system for intelligent transport systems were successfully demonstrated.

Saliency Detection with Moving Camera via Background Model Completion.

Detecting saliency in videos is a fundamental step in many computer vision systems. Saliency is the significant target(s) in the video. The object of interest is further analyzed for high-level applications. The segregation of saliency and the background can be made if they exhibit different visual cues. Therefore, saliency detection is often formulated as background subtraction. However, saliency detection is challenging. For instance, dynamic background can result in false positive errors. In another scenario, camouflage will result in false negative errors. With moving cameras, the captured scenes are even more complicated to handle. We propose a new framework, called saliency detection via background model completion (SD-BMC), that comprises a background modeler and a deep learning background/foreground segmentation network. The background modeler generates an initial clean background image from a short image sequence. Based on the idea of video completion, a good background frame can be synthesized with the co-existence of changing background and moving objects. We adopt the background/foreground segmenter, which was pre-trained with a specific video dataset. It can also detect saliency in unseen videos. The background modeler can adjust the background image dynamically when the background/foreground segmenter output deteriorates during processing a long video. To the best of our knowledge, our framework is the first one to adopt video completion for background modeling and saliency detection in videos captured by moving cameras. The F-measure results, obtained from the pan-tilt-zoom (PTZ) videos, show that our proposed framework outperforms some deep learning-based background subtraction models by 11% or more. With more challenging videos, our framework also outperforms many high-ranking background subtraction methods by more than 3%.

Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales.

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

Effects of differentiation on peak intensity in direct Auger spectra

A companion paper in this issue of Surface and Interface Analysis (Surf Interface Anal. 2021;1–6. doi: 10.1002/sia.7029) discusses corrections for the effects of analyzer modulation on peak intensity in derivative Auger spectra. Today, most Auger spectra are acquired in the direct mode, E·N(E), and derivatives are taken using a computer program to measure peak intensities (peak‐to‐peak heights) with the background suppressed, for ultimate quantification. In taking a derivative, there is sometimes a choice of the algorithm used for taking the derivative, but the most common method is to use the Savitzky–Golay algorithm. In using the Savitzky–Golay algorithm, there is also a choice in the number of data points used in the differentiation. To improve quantification, Martin Seah suggested using relatively large Gaussian broadening in the differentiation process to reduced effects of line shape differences in measuring signal intensities, as often occur due to chemical effects on spectra. This paper shows the effects of the number of points used with the Savitzky–Golay algorithm in taking the derivative on peak‐to‐peak heights in derivative Auger spectra and how dynamic background subtraction can be used to obtain Auger peak area values independent of the broadening of spectra caused by such differentiation.

Research on Population Development Trend in Huizhou of China Forecast Based on Optimal Weighted Combination Method and Fractional Grey Model

In this paper, the optimal weighted combination model and fractional grey model are constructed. The coefficients of the optimal weighted combination model are determined by minimizing the sum of squares of resists of each model. On the other hand, the optimal conformable fractional order and dynamic background value coefficient are determined by the quantum inspired evolutionary algorithm (QIEA). Taking the resident population from 2008 to 2018 as the research object, the optimal weighted combination model and fractional grey model were used to study the estimated and predicted values. The results are compared and analyzed. The results show that the fractional grey model is better than the optimal weighted combination model in the estimation of the values. The optimal weighted combination model is better than the fractional grey model in predicting. Meanwhile, the fractional grey model is found to be very suitable for the data values that are large, and the changes between the data are relatively small. The research results expand the application of the fractional grey model and have important implications for the policy implementation activities of Huizhou government according to the population growth trend in Huizhou.

Luminescence of Baikal water as a dynamic background of the Baikal-GVD Neutrino Telescope

The Baikal-GVD is a neutrino telescope situated in the deepest freshwater lake in the world — Lake Baikal. The design of the Baikal-GVD trigger system allows also to study the ambient light of the lake. The analysis of the optical light activity of Baikal water, particularly, time and spatial variations of the luminescence activity for data collected in years 2018, 2019, and 2020 is presented. For the first time we observed highly luminescent layer moving upwards with maximal speed of 28 m/day in January 2021.

Corrections for the effects of analyzer modulation on peak intensity in derivative Auger spectra

Starting in the mid‐1960s, the detection and display of peaks in Auger electron spectroscopy (AES) were improved by using modulation of the electron energy analyzer coupled with electron detection using a lock‐in amplifier. This allowed a derivative of the electron energy distribution, N(E), to be obtained directly at the output of the lock‐in amplifier thereby removing most of the effect from the relatively large, slowly varying, electron background signal due to secondary and backscattered electrons. For relatively low modulation amplitudes, the peak‐to‐peak intensity of the Auger features increased linearly with modulation amplitude (for a deflection‐type analyzer), improving the signal‐to‐noise ratio. However, with relatively large modulations, the Auger peak shapes distorted, and the peak‐to‐peak heights eventually decreased in size, and this nonlinearity would cause problems in quantitative analysis. A universal curve was developed for singlet Auger peaks to approximate corrections due to this peak distortion, but an approach to exactly correct for such distortions was largely ignored by the AES community. This approach was called Dynamic Background Subtraction and is even relevant today as some Auger instruments using modulation and lock‐in amplifiers are still being manufactured. This review paper describes approximate and exact corrections for modulation effects in AES data.

A dynamic noise background reveals perceptual motion extrapolation: The twinkle-goes illusion.

We find that on a dynamic noise background, the perceived disappearance location of a moving object is shifted in the direction of motion. This “twinkle-goes” illusion does not require luminance- or chromaticity-based confusability of the object with the background, or on the amount of background motion energy in the same direction as the object motion. This suggests that the illusion is enabled by the dynamic noise masking the offset transients that otherwise accompany an object's disappearance. While these results are consistent with an anticipatory process that pre-activates positions ahead of the object's current position, additional findings suggest an alternative account: a continuation of attentional tracking after the object disappears. First, the shift increased with speed until over 1.2 revolutions per second (rps), nearing the attentional tracking limit. Second, the shift was greatly reduced when attention was divided between two moving objects. Finally, the illusion was associated with a delay in simple reaction time to the disappearance of the object. We propose that in the absence of offset transients, attentional tracking keeps moving for several tens of milliseconds after the target disappearance, and this causes one to hallucinate a moving object at the position of attention.