Focus Window Research Articles

Addressing bias in the use of buffers for focal and geographically weighted analyses

Focal analyses (also known as ‘buffer’ or ‘neighbourhood’ analyses) seek to characterise a location based on its surroundings and are commonplace in GIS applications across many fields and disciplines. However, the implicit assumptions made by researchers in these analyses result in an unintended bias towards the periphery of the focal window, which we term Focal Area Bias (FAB). FAB can have a substantial impact upon the resulting values, and in the most extreme cases can result in paradoxical outcomes. Where geographical weighting functions are used, the interaction between the weighting function and FAB means that it will not have the expected effect, leading to the misinterpretation of results. This research characterises the issue of FAB, before presenting a corrective function to remove it. The efficacy of the proposed corrective function and the spatial characteristics of FAB are then evaluated to demonstrate the importance of this issue. We recommend that researchers and practitioners should consider the impact of FAB when undertaking focal analysis and make use of the corrective functions presented here to remove this issue, particularly where geographical weighting is desired.

Concomitant Use of Oral Anticoagulants with Oral Dipeptidyl Peptidase-4 Inhibitors and Serious Bleeding Events.

In a prior screening study, saxagliptin, a dipeptidyl peptidase-4 inhibitor (DPP-4i), was found to have an increased rate of serious bleeding when used concomitantly with several oral anticoagulants (OACs). We aimed to confirm or refute the associations between concomitant use of individual OACs and DPP-4is and serious bleeding in a large US database, using self-controlled case series (SCCS) and case-crossover (CCO) designs. The study population was eligible Medicare beneficiaries co-exposed to a DPP-4i (precipitant) and either an OAC (object drug) or lisinopril (negative control object drug) in 2016-2020. For the SCCS, we used conditional Poisson regression to estimate adjusted rate ratios (RRs) between each co-exposure (vs. not) and serious bleeding and divided the RR by the adjusted RR for the corresponding lisinopril + precipitant pair to obtain ratios of RRs (RRRs). For the CCO, we estimated the adjusted odds ratios (ORs) of exposure to the precipitant in the focal window vs. referent window using multivariable conditional logistic regression and divided the ORs in the object drug-exposed cases over the ORs in negative object drug-exposed cases to obtain the ratios of ORs (RORs). The adjusted RRRs for serious bleeding ranged from 0.32 (0.05-1.91) for apixaban/lisinopril + saxagliptin to 3.49 (1.29-9.48) for warfarin/lisinopril + linagliptin. The adjusted RORs ranged from 0.01 (0.00-0.20) for rivaroxaban/lisinopril + saxagliptin to 2.99 (0.74-12.11) for apixaban/lisinopril + linagliptin. While we could not confirm previously identified signals because of statistical imprecision, several numerically elevated estimates still warrant caution in concomitant use and further examination.

Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods.

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.

Analysis and comparison of automatic image focusing algorithms in digital image processing

Optical imaging equipment is widely used in many fields such as industry, medicine and military, etc. The autofocus, as the main component affecting image clarity, still needs to be optimized and improved. A simpler passive autofocus system is selected, and a grayscale covariance matrix is introduced to use its output of inter-pixel grayscale relationship data to attenuate the influence of noise and realize the visual measurement of image quality. Next, the focus window locking is optimized, and a first-order moment window method incorporating image difference shadow is proposed, in which the Gaussian blur can enhance the smoothness of the image, and the difference shadow map is obtained according to the difference between the original image and the smoothed image, and the method keyed out the background of the image to enhance the detail portrayal of the target region, so as to locate the center of focus, and finally the window locking is realized in accordance with the proposed size. The study verifies the performance of the focusing model through simulation experiments, which show that the mean value of the accuracy of the model is the best value of 0.96 among the control models in the face of the Gaussian noise of (0.1,0.004). Therefore, this autofocus model can effectively improve the autofocus efficiency.

Maximum Gradient Autofocus Technology of Microsporidia Images Based on Color Feature

There are many impurities in the microscopic images of extracted microsporidia samples of Bombyx mori pebrine, and Bombyx mori pebrine with elliptical symmetric shape has certain fluidity and obvious stratification. Traditional focusing methods cannot accurately locate the main regions of microsporidia images, and the focusing effect is poor. On this basis, an automatic focusing method combining the microsporidia image features and the evaluation and determination of maximum gradient direction is proposed. First, the HSV color space with stable color information is used to extract the suspected positions of microsporidia targets, so that the interference of some impurities under complex backgrounds is removed and the redundancy of image content calculation is reduced. Then, combined with the light green features of Bombyx mori pebrine, the G-component gray image of microsporidia in the RGB color space is used to extract the significant gradient region. A dynamic focus window is constructed to accurately locate the target region and reduce the influence of microsporidia flow on the focus evaluation function and the bimodal interference caused by impurities. Finally, the maximum second-order difference is obtained through the four-dimensional gradient distribution, and the focus sharpness evaluation function is formulated to adapt to the microsporidia shape and improve the sensitivity of the focus function. The experiments show that under the dynamic window of microsporidia color gradient of different samples, the sharpness ratio and the highest sensitivity factor of the focus evaluation function proposed in this paper can reach 0.06341 and 0.95, respectively. It can meet the accurate and sensitive autofocus of microscopic images of color microsporidia samples under complex backgrounds.

Simulating burn severity maps at 30 meters in two forested regions in California

Climate change is altering wildfire and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size. The ability to predict future burn severity patterns could better support policy and land management decisions. Here we demonstrate a methodology to first, statistically estimate individual burn severity classes at 30 meters and second, cluster and smooth high severity patches onto a known landscape. Our goal here was not to exactly replicate observed burn severity maps, but rather to utilize observed maps as one realization of a random process dependent on climate, topography, fire weather, and fuels, to inform creation of additional realizations through our simulation technique. We developed two sets of empirical models with two different vegetation datasets to test if coarse vegetation could accurately model for burn severity. While visual acuity can be used to assess the performance of our simulation process, we also employ the Ripley’s K function to compare spatial point processes at different scales to test if the simulation is capturing an appropriate amount of clustering. We utilize FRAGSTATS to obtain high severity patch metrics to test the contiguity of our high severity simulation. Ripley’s K function helped identify the number of clustering iterations and FRAGSTATS showed how different focal window sizes affected our ability to cluster high severity patches. Improving our ability to simulate burn severity may help advance our understanding of the potential influence of land and fuels management on ecosystem-level response variables that are important for decision-makers. Simulated burn severity maps could support managing habitat and estimating risks of habitat loss, protecting infrastructure and homes, improving future wildfire emissions projections, and better mapping and planning for fuels treatment scenarios.

Does administration of vaginal estrogens increase the risk of venous thromboembolism: A case-control study.

Does administration of vaginal estrogens increase the risk of venous thromboembolism: A case-control study.

Association between opioid-related deaths and prescribed opioid dose and psychotropic medicines in England: a case-crossover study

BackgroundOpioid-overdose deaths are associated with poisoning with prescription and illicit opioids in the USA. In contrast, opioid-related deaths (ORDs) in the UK often involve drugs and substances of misuse, and may not be associated with a high dose of prescribed opioids. This study aimed to investigate the association between prescribed opioid dose and ORDs in UK primary care. MethodsThis case-crossover study used the Clinical Practice Research Datalink and death registration between 2000 and 2015 to identify ORDs. Daily oral morphine equivalent (OMEQ) dose was measured within a 90 day focal window before ORD and three earlier reference windows. Conditional logistic regression models assessed the adjusted odds ratio (aOR) and 95% confidence interval (95% CI) comparing daily OMEQ dose greater than 120 mg in the focal window against the reference windows. ResultsOf the 232 ORDs, 62 (26.7%) were not prescribed opioids in the year before death. Of the remaining 170 cases, 50 (29.4%) were never prescribed a daily OMEQ dose greater than 50 mg. Daily OMEQ doses over 120 mg (aOR 2.20; 95% CI: 1.06–4.56), co-prescribing gabapentinoids (aOR 2.32; 95% CI: 1.01–5.33), or some antidepressants (aOR 3.03; 95% CI: 1.02–9.04) significantly increased the risk of ORD. ConclusionsDaily OMEQ dose greater than 120 mg and the concomitant use of psychotropic medicines were related to ORDs in the UK. Prescribers should cautiously avoid prescribing opioids with a daily OMEQ dose greater than 120 mg day−1 and the combination of opioids and gabapentinoids, even with low opioid doses.

Deep Reinforcement Learning for Guidewire Navigation in Coronary Artery Phantom

In percutaneous intervention for treatment of coronary plaques, guidewire navigation is a primary procedure for stent delivery. Steering a flexible guidewire within coronary arteries requires considerable training, and the non-linearity between the control operation and the movement of the guidewire makes precise manipulation difficult. Here, we introduce a deep reinforcement learning(RL) framework for autonomous guidewire navigation in a robot-assisted coronary intervention. Using Rainbow, a segment-wise learning approach is applied to determine how best to accelerate training using human demonstrations with deep Q-learning from demonstrations (DQfD), transfer learning, and weight initialization. `State' for RL is customized as a focus window near the guidewire tip, and subgoals are placed to mitigate a sparse reward problem. The RL agent improves performance, eventually enabling the guidewire to reach all valid targets in `stable' phase. Our framework opens anew direction in the automation of robot-assisted intervention, providing guidance on RL in physical spaces involving mechanical fatigue.

基于自适应对焦窗口的计算鬼成像目标深度估计方法

基于自适应对焦窗口的计算鬼成像目标深度估计方法

Depth estimation in computational ghost imaging system using auto-focusing method with adaptive focus window

Depth estimation in computational ghost imaging system using auto-focusing method with adaptive focus window

Improved autofocus method for human red blood cell images.

This paper presents an improved autofocus method for human red blood cell images in a microscope. The products of the sum modulus difference and the real-valued fast Fourier transform function are multiplied to obtain an improved sharpness evaluation using the properties of a Gaussian function. It is superior to traditional evaluations in terms of unimodality, steepness, and sensitivity. A new quantitative criterion is proposed to represent the ability of sharpness evaluation against noise. An adaptive focus window with great robustness is proposed that can reduce the computation cost and adverse effects of the background. The better performances of the proposed algorithms are all proved by experiment results, and they can help to find the quasi-focus position more quickly and accurately.

Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models

AbstractAimBats are important components of mammalian biodiversity and strong bioindicators, but their fine‐scale distributions often remain less known than other taxa (e.g., plants, birds). Yet as highly mobile species with multiple needs in the landscape, bats impose serious modelling challenges, such as advanced use of neighbourhood analyses. The aims of this study were to test the use of a designed sampling of bats for biodiversity and conservation assessments, and to find appropriate modelling solutions for providing nature practitioners with reliable potential bat distribution maps in a mountain area of high conservation interest.LocationThe western Swiss Alps of Vaud.MethodsWe conducted a one‐year field survey combining passive acoustic recordings supplemented by mist net catching to collect data on bats. These data were then used to create univariate models with focal land use/cover variables using different focal window sizes to detect the optimal species‐specific scale of influence for each variable. The large number of selected variables was then used to create ensembles of small models at a 100 m × 100 m resolution, and the resulting habitat suitability maps were transformed into species distribution maps for practitioners.ResultsWe were able to collect data to model 14 different bat species representing 66% of the Swiss bat diversity, including four red list species. In general, the most important variables were Euclidean distance to road or water, temperature and slope, but there was large variation among species both for the variable importance and for the optimal focal window size.Main conclusionOur study greatly increased the knowledge of bats in this region and showed that many of the red list species are nowadays disappearing from the intensively used lowland plains and restricted to the remaining forests along the slopes. Additionally, we highlighted the importance of selecting the variable scale on a species‐specific basis accounting for their mobility and range sizes.

Applications of improved artificial fish swarm algorithm in microscopy autofocus

The selection of the focusing window is the key procedure in achieving precise automatic focus of the microscope. For the traditional focus window selection method, the limitation is that the target object cannot be accurately positioned. This paper proposes an improved artificial fish focusing window method. The method takes the area-of-interest of the whole image as the basis of the selection window. Through utilizing the global optimization ability of the artificial fish swarm algorithm, the best focusing window can be obtained. Adding the global optimal value to the behavior update of each artificial fish makes the artificial fish quickly move to the optimal position. Under the premise of ensuring accuracy, the elimination behavior is introduced to improve the convergence speed of the algorithm in the later period. Furthermore, according to the characteristics of the bulletin board in the algorithm, the interference area is identified with the trend comparison method, and the influence of the non-target area is effectively excluded. Experiment results show that the focusing window obtained by this algorithm can be well-suited for the target object, greatly improve the accuracy of autofocus, and make the efficiency improvement 1.65 times than the traditional method.

Auto-focusing approach on multiple micro objects using the prewitt operator

In this paper, we propose an auto focusing method to find the best plane of focus amongst different depth micro objects. By achieving this, the ultimate goal of the project will be autofocusing on motile micro-organisms to check their quality, e.g., semen motility. Firstly, the auto focusing system cycles through the rough focal range of the micro objects, which represents the cell culture by moving in steps of 2 $$\upmu \hbox {m}$$ towards best focus, passing it, and completing the scanning process. Simultaneously, at each step, an image of the sample is collected and transformed by use of a Prewitt edge detection operator in order to ascertain focal quality. Secondly, the system moves to the best focus position that is calculated via Prewitt edge detection approach. Thirdly, to begin actively monitoring the focal plane, the system makes minute adjustments to keep the target objects in-focus. This accounts for any changes in the culture, such as cells moving in and out of the focal window or up and down within the semen fluid. The Prewitt operator is tested for focusing performance on multi-microobjects at different focal planes and is compared with other edge detection gradient functions to confirm superiority.

Fish swarm window selection algorithm based on cell microscopic automatic focus

The selection window of selection algorithm used in traditional automatic focus window concentrates mainly on the center of image, so the randomly distributed cells would always be out of focus. To address this problem, on the basis of analyzing the performance of selection algorithm of different focusing windows, a modified auto-focus window algorithm upon traditional fish swarm algorithm has been proposed: fish swarm window selection algorithm. After comparatively analyzing the images in focus window that are obtained by traditional and improved fish swarm algorithm, a conclude can be drawn that the focus window of modified algorithm can contain more cells and target bodies. To be specific, owing to fish-swarm window selection algorithm, in the selection window the quantity of the high frequency of images greatly increases, the optimal solution converges to 0.999, and the estimated value of sharpness of the obtained microscopic cell images also improves with high precision and high accuracy of focus of the improved algorithm.

结合视觉感知的调焦窗口构建

In order to build right focus window for optical measure equipment in shooting range, a novel method combined with visual perception is proposed. It can cut down compute time and overcome disturbance of background during auto-focus procedure based on image. The image is separated into several sub-images by using pyramid structure. The function and principle of cells in human visual cortex are simulated by Gabor filter and difference of Gaussian (DoG) model. Feature maps from the sub-images are extracted and combined together to get final feature map based on visual perception. The threshold is used to denoise disturbance in the feature map and build regular rectangle focus window area by spreading boundary. After plenty of experiments, the results show that this method can build focus window rapidly with exact size and right position for the target in image. And it is available for every kinds of focus states, especially for deep-defocus state. To deal with the image of 720 pixel576 pixel, the compute time is less than 130 ms. The proposed method can construct steady and exact focus window during the whole focus procedure, which satisfies the practical demand of shooting range and is of great and wide value for engineering application.

The Relation of Configural and Feature Face Information Under the Condition of Holistic Process

Although it is acknowledged that human face is processed holistically, little is known about how global configural information interacting with local feature information during holistic face processing. To address this issue, we measured subjects' performance in a face learning and identification task. Sixty-three college students participated in this study which consisted of three experiments. In experiment 1, thirty-six subjects were asked to give subjective scores of face clarity modulated through variant low-pass spatial filters. Five low-pass filter levels were chosen for experiment 2 and 3. In experiment 2, fourteen subjects were instructed to identify which testing face picture was previously presented at the beginning of the task. The test pictures were modulated at two levels. At global level, low-pass filters were applied on the whole face; at local level, one of four face features was masked by a focus window containing corresponding non-filter feature. The distracting testing picture was masked at different face feature from target picture. A repeated two-factor (5 low-pass filter levels × 4 face features) ANOVA analysis was conducted to analyze the behavior performance data. In experiment 3, thirteen subjects performed the same task except for using inverted face instead. And same ANOVA analysis was used to analyze the performance data. The results of experiment 2 revealed that the processing of local features within the focus window was more difficult when global configural information outside of the window filtered at lower level. The similar pattern was also observed in experiment 3. These findings support the holistic face processing and further suggest the competing relation between global configural and local feature information processing. In addition, whether face processing is based on configural or feature information depends on the extracting difficulties of feature information. We have found the interacting pattern between global configural information and local feature information during holistic face processing. This specific pattern shows that the crucial information type for sucessful face processing is dynamic changed depending on the information extracting difficulties, which indicates the adaptability of human visual perception system.

Scale dependent inference in landscape genetics

Ecological relationships between patterns and processes are highly scale dependent. This paper reports the first formal exploration of how changing scale of research away from the scale of the processes governing gene flow affects the results of landscape genetic analysis. We used an individual-based, spatially explicit simulation model to generate patterns of genetic similarity among organisms across a complex landscape that would result given a stipulated landscape resistance model. We then evaluated how changes to the grain, extent, and thematic resolution of that landscape model affect the nature and strength of observed landscape genetic pattern–process relationships. We evaluated three attributes of scale including thematic resolution, pixel size, and focal window size. We observed large effects of changing thematic resolution of analysis from the stipulated continuously scaled resistance process to a number of categorical reclassifications. Grain and window size have smaller but statistically significant effects on landscape genetic analyses. Importantly, power in landscape genetics increases as grain of analysis becomes finer. The analysis failed to identify the operative grain governing the process, with the general pattern of stronger apparent relationship with finer grain, even at grains finer than the governing process. The results suggest that correct specification of the thematic resolution of landscape resistance models dominates effects of grain and extent. This emphasizes the importance of evaluating a range of biologically realistic resistance hypotheses in studies to associate landscape patterns to gene flow processes.