scholarly journals On the Improvement of Multiple Circles Detection from Images Using Hough Transform

2019 ◽  
Vol 20 (2) ◽  
pp. 331
Author(s):  
Wesley Oliveira Barbosa ◽  
Antonio Wilson Vieira
Keyword(s):  
Hough Transform ◽  
A Priori ◽  
False Positives ◽  
Multiple Objects ◽  
Efficient Tool ◽  
Robust Detection ◽  
Point Set ◽  
Priori Information ◽  
The Individual ◽  
Set Of Points

The automatic detection of lines and curves from color images is a very important task in many applications, such as object recognition and scene reconstruction. Although there are closed formulation for curve fitting to a set of points, if the point set describes more than one instance of the object, as two circles for example, there is no closed formulation for obtaining the individual set of parameters without a priori information of which points belong to each object. However, it is usual the presence of multiple instances of objects such as lines and circles on an image. The well known Hough Transform is an efficient tool for recovering multiple objects from images using a voting process where the usual presence of false positives is an issue. In our work, we present an improvement on the voting process to detect multiple circles using Hough Transform in order to avoid false positives. Our experiments show that our voting process leads to a more robust detection, reducing the number of false positive and providing a more accurate detection even with large number of circles.

scholarly journals Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving

2021 ◽  
Vol 10 (11) ◽  
pp. 761
Author(s):  
Tengfei Yu ◽  
He Huang ◽  
Nana Jiang ◽  
Tri Dev Acharya
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Autonomous Driving ◽  
Relative Accuracy ◽  
Accuracy Evaluation ◽  
High Definition ◽  
Verification Method ◽  
Road Signs ◽  
Point Set ◽  
Priori Information

High-definition maps (HDM) for autonomous driving (AD) are an important component of AD systems. HDMs accurately provide a priori information, including lane lines, and road signs, for AD systems. It is an important task to make a reasonable accuracy assessment of the HDM. The current methods for relative accuracy evaluation of general maps in the field of mapping are not fully applicable to HDMs. In this study, a method based on point set alignment and resampling is used to evaluate the relative accuracy of lane lines, and experiments are conducted based on relevant real HDM data. The results show that the relative accuracy of the lane lines is more detailed and relevant than the traditional method. This has implications for the quality control of HDM production.

Impacts of A Priori Databases Using Six WRF Microphysics Schemes on Passive Microwave Rainfall Retrievals

2013 ◽  
Vol 30 (10) ◽  
pp. 2367-2381 ◽  
Author(s):  
Ju-Hye Kim ◽  
Dong-Bin Shin ◽  
Christian Kummerow
Keyword(s):  
A Priori ◽  
Wrf Model ◽  
Tropical Rainfall Measuring Mission ◽  
Cloud Microphysics ◽  
Passive Microwave ◽  
A Priori Information ◽  
Physically Based ◽  
Double Moment ◽  
Priori Information ◽  
The Individual

Abstract Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.

scholarly journals Retrieval of Canopy component temperatures through Bayesian inversion of directional thermal measurements

2009 ◽  
Vol 6 (2) ◽  
pp. 3007-3040 ◽  
Author(s):  
J. Timmermans ◽  
W. Verhoef ◽  
C. van der Tol ◽  
Z. Su
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
A Priori ◽  
Inversion Method ◽  
Success Rates ◽  
Brightness Temperatures ◽  
Ill Posed ◽  
Priori Information ◽  
The Individual ◽  
High Level

Abstract. In remote sensing evapotranspiration is estimated using a single surface temperature. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (ℜspectra≈0.3, ℜgonio≈0.3, and ℜAATSR≈0.5), and no improvement using mono-angular sensors (ℜ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K), but for low LAI values the measurement setup provides extra disturbances in the directional brightness temperatures, RMSEyoung maize=2.85 K, RMSEmature maize=2.85 K. As these disturbances, were only present for two crops and can be eliminated using masked thermal images the method is considered successful.

scholarly journals Challenging a bioinformatic tool’s ability to detect microbial contaminants usingin silicowhole genome sequencing data

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3729 ◽  
Author(s):  
Nathan D. Olson ◽  
Justin M. Zook ◽  
Jayne B. Morrow ◽  
Nancy J. Lin
Keyword(s):  
Binary Mixtures ◽  
Genome Sequencing ◽  
Pathogen Detection ◽  
A Priori ◽  
High Sensitivity ◽  
False Positives ◽  
Sequencing Data ◽  
Materials Used ◽  
The Individual ◽  
Genome Assemblies

High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need fora prioriassumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, withStaphylococcus,Escherichia, andShigellahaving the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in thein-silicodatasets at the equivalent of 1 in 1,000 cells, thoughF. tularensiswas not detected in any of the simulated contaminant mixtures andY. pestiswas only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

scholarly journals Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements

2009 ◽  
Vol 13 (7) ◽  
pp. 1249-1260 ◽  
Author(s):  
J. Timmermans ◽  
W. Verhoef ◽  
C. van der Tol ◽  
Z. Su
Keyword(s):  
Sensitivity Analysis ◽  
A Priori ◽  
Inversion Method ◽  
Success Rates ◽  
Brightness Temperatures ◽  
Single Temperature ◽  
Ill Posed ◽  
Priori Information ◽  
The Individual ◽  
High Level

Abstract. Evapotranspiration is usually estimated in remote sensing from single temperature value representing both soil and vegetation. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (Srspectra≈0.3, Srgonio≈0.3, and SrAATSR≈0.5), and no improvement using mono-angular sensors (Sr≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K); but for low LAI values the results were unsatisfactory (RMSEyoung maize=2.85 K). This discrepancy was found to originate from the presence of the metallic construction of the setup. As these disturbances, were only present for two crops and were not present in the sensitivity analysis, which had a low LAI, it is concluded that using masked thermal images will eliminate this discrepancy.

The ILRS Contribution to ITRF2020

2021 ◽  
Author(s):  
Vincenza Luceri ◽  
Erricos C. Pavlis ◽  
Antonio Basoni ◽  
David Sarrocco ◽  
Magdalena Kuzmicz-Cieslak ◽  
...  
Keyword(s):  
Time Series ◽  
Tracking System ◽  
A Priori ◽  
Systematic Errors ◽  
Novel Approach ◽  
Priori Information ◽  
The Stability ◽  
The Individual ◽  
Stability And Consistency

<p>The International Laser Ranging Service (ILRS) contribution to ITRF2020 has been prepared after the re-analysis of the data from 1993 to 2020, based on an improved modeling of the data and a novel approach that ensures the results are free of systematic errors in the underlying data. This reanalysis incorporates an improved “target signature” model (CoM) that allows better separation of true systematic error of each tracking system from the errors in the model describing the target’s signature. The new approach was developed after the completion of ITRF2014, the ILRS Analysis Standing Committee (ASC) devoting almost entirely its efforts on this task. The robust estimation of persistent systematic errors at the millimeter level permitted the adoption of a consistent set of long-term mean corrections for data collected in past years, which are now applied a priori (information provided by the stations from their own engineering investigations are still taken into consideration). The reanalysis used these corrections, leading to improved results for the TRF attributes, reflected in the resulting new time series of the TRF origin and especially in the scale. Seven official ILRS Analysis Centers computed time series of weekly solutions, according to the guidelines defined by the ILRS ASC. These series were combined by the ILRS Combination Center to obtain the official ILRS product contribution to ITRF2020.</p><p>The presentation will provide an overview of the analysis procedures and models, and it will demonstrate the level of improvement with respect to the previous ILRS product series; the stability and consistency of the solution are discussed for the individual AC contributions and the combined SLR time series.</p>

scholarly journals Envisat MIPAS measurements of CFC-11: retrieval, validation, and climatology

2008 ◽  
Vol 8 (2) ◽  
pp. 4561-4602 ◽  
Author(s):  
L. Hoffmann ◽  
M. Kaufmann ◽  
R. Spang ◽  
R. Müller ◽  
J. J. Remedios ◽  
...  
Keyword(s):  
Large Scale ◽  
Lower Stratosphere ◽  
A Priori ◽  
Michelson Interferometer ◽  
Optimal Estimation ◽  
Atmospheric Conditions ◽  
Data Set ◽  
Mixing Ratios ◽  
Priori Information ◽  
The Individual

Abstract. From July 2002 to March 2004 the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard the European Space Agency's Environmental Satellite (Envisat) measured nearly continuously mid infrared limb radiance spectra. These measurements are utilised to retrieve the global distribution of the chlorofluorocarbon CFC-11 by applying a new fast forward model for Envisat MIPAS and an accompanying optimal estimation retrieval processor. A detailed analysis shows that the total retrieval errors of the individual CFC-11 volume mixing ratios are typically below 10% and that the systematic components are dominating. Contribution of a priori information to the retrieval results are less than 5 to 10%. The vertical resolution of the observations is about 3 to 4 km. The data are successfully validated by comparison with several other space experiments, an air-borne in-situ instrument, measurements from ground-based networks, and independent Envisat MIPAS analyses. The retrieval results from 425 000 Envisat MIPAS limb scans are compiled to provide a new climatological data set of CFC-11. The climatology shows significantly lower CFC-11 abundances in the lower stratosphere compared with the Reference Atmospheres for MIPAS (RAMstan V3.1) climatology. Depending on the atmospheric conditions the differences between the climatologies are up to 30 to 110 ppt (45 to 150%) at 19 to 27 km altitude. Additionally, time series of CFC-11 mean abundance and variability for five latitudinal bands are presented. The observed CFC-11 distributions can be explained by the residual mean circulation and large-scale eddy-transports in the upper troposphere and lower stratosphere. The new CFC-11 data set is well suited for further scientific studies.

scholarly journals Air delay propagation patterns in Europe from 2015 to 2018: an information processing perspective

2021 ◽  
Author(s):  
Luisina Pastorino ◽  
Massimiliano Zanin
Keyword(s):  
Information Processing ◽  
A Priori ◽  
Future Research ◽  
Negative Effects ◽  
Delay Propagation ◽  
Complementary Approach ◽  
Priori Information ◽  
The Individual ◽  
The Cost

Abstract The characterisation of delay propagation is one of the major topics of research in air transport management, due to its negative effects on the cost-efficiency, safety and environmental impact of this transportation mode. While most research works have naturally framed it as a transportation process, the successful application of network theory in neuroscience suggests a complementary approach, based on describing delay propagation as a form of information processing. This allows reconstructing propagation patterns from the dynamics of the individual elements, i.e. from the evolution observed at individual airports, without the need of additional a priori information. We here apply this framework to the analysis of delay propagation in the European airspace between 2015 and 2018, describe the evolution of the observed structure, and identify the role of individual airports in it. We further use this analysis to illustrate the limitations and challenges associated to this approach, and to sketch a roadmap of future research in this evolving topic.

scholarly journals Predicting forest stand variables from airborne LiDAR data using a tree detection method in Central European forests

2019 ◽  
Vol 65 (3-4) ◽  
pp. 191-197 ◽  
Author(s):  
Ivan Sačkov ◽  
Ľubomír Scheer ◽  
Tomáš Bucha
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Individual Tree ◽  
Tree Detection ◽  
Detection Approach ◽  
Mean Diameter ◽  
Priori Information ◽  
The Individual

Abstract In this study, the individual tree detection approach (ITD) was used to estimate forest stand variables, such as mean height, mean diameter, and total volume. Specifically, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia) which uses all the information contained in the original point cloud and a priori information. For the accuracy assessment, four reference forest stands with different types of species mixture and the area of 7.5 ha were selected and measured. Furthermore, independent measurements of 1 372 trees were made for the construction of allometric models. The author’s ITD-based method provided slightly more accurate estimations for stands with substantial or moderate dominance of coniferous trees. However, no statistically significant effect of species mix on the overall accuracy was confirmed (p < 0.05). The root mean square error did not exceed 1.9 m for mean height, 3.0 cm for mean diameter, and 12.88 m3 ha−1 for total volume.

Fluorescence Anisotropy Selective Technique (FAST): A New Approach to Multicomponent Fluorimetric Analysis

1988 ◽  
Vol 42 (7) ◽  
pp. 1245-1250 ◽  
Author(s):  
Frank V. Bright
Keyword(s):  
Fluorescence Anisotropy ◽  
Rotational Diffusion ◽  
A Priori ◽  
Emission Spectra ◽  
Fluorescence Lifetimes ◽  
New Approach ◽  
Fluorimetric Analysis ◽  
Priori Information ◽  
The Individual ◽  
Selective Technique

A new analytical approach to multicomponent fluorimetric analysis is presented theoretically and is experimentally demonstrated. The new approach, termed fluorescence anisotropy selective technique (FAST), allows one to directly resolve the individual components in complex mixtures on the basis of differences in the rotational diffusion rates of the components. Importantly, unlike steady-state polarization-based approaches, FAST does not require a priori information regarding the number of individual components. By the use of FAST, the individual emission spectra of the same fluorophore in two different microenvironments can be simultaneously determined even if the fluorescence lifetimes and emission spectra of the two components are extremely similar. To demonstrate the power of FAST, we show results for the simultaneous resolution of dansylamide bound to beta-cyclodextrin and human serum albumin. In this example, the fluorescence lifetimes of the two components are essentially identical.

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