Robust and High Accuracy Algorithm for Detection of Pupil Images

Recently,many researchers have tried to develop a robust,fast,and accurate algorithm.This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking,gaze-based human-computer interaction,medical applications(such as deaf and diabetes patients),and attention analysis.Many real-world conditions challenge the eye appearance,such as illumination,reflections,and occasions.On the other hand,individual differences in eye physiology and other sources of noise,such as contact lenses or make-up.The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications.The proposed circular hough transform with morphing canny edge detection for Pupillometery(CHMCEP)algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy.The performance of the proposed CHMCEP algorithm was tested against recent pupil detection methods.Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11,78.54,58,and 78 according to´Swirski,ExCuSe,Else,and labeled pupils in the wild(LPW)data sets,respectively.These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Assessing 10 Satellite Precipitation Products in Capturing the July 2021 Extreme Heavy Rain in Henan, China

On 20 July 2021,a sudden rainstorm happened in central and northern Henan Province,China,killing at least 302people.This extreme precipitation event incurred substantial socioeconomic impacts and resulted in serious losses.Accurate monitoring of such rainstorm events is crucial.In this study,qualitative and quantitative methods are used to comprehensively evaluate the abilities of 10 high-resolution satellite precipitation products[CMORPH-Raw(Climate Prediction Center morphing technique),CMORPH-RT,PERSIANN-CCS(Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks),GPM IMERG-Early(Integrated Multisatellite Retrievals for Global Precipitation Measurement),GPM IMERG-Late,GSMaP-Now(Global Satellite Mapping of Precipitation),GSMaP-NRT,FY-2F,FY-2G,and FY-2H]in capturing this extreme rainstorm event,as well as their performances in monitoring different precipitation intensities.The results show that these satellite precipitation products are able to capture the spatial distributions of the rainstorm(e.g.,its location in central and northern Henan),but all products have underestimated the amount of precipitation in the rainstorm center.With the increase in precipitation intensity,the hit rate decreases,the threat score decreases,and the false alarm rate increases.CMORPH-RT is better at capturing the rainstorm than CMORPH-Raw,and it depictes the rainstorm process well;GPM IMERG-Late is more accurate than GPM IMERG-Early;GSMaP-NRT has performed better than GSMaP-Now;and PERSIANNCCS and FY-2F perform poorly.Among the products,CMORPH-RT performs the best,which has accurately captured the center of the rainstorm,and is also the closest to the station-based observations.In general,the satellite precipitation products that integrate infrared and passive microwave data are found to be better than those that only make use of infrared data.The satellite precipitation retrieval algorithm and the amount of passive microwave data have a relatively greater impact on the accuracy of satellite precipitation products.