New object detection features in the OpenCV library

Druzhkov, P. N.; Erukhimov, Victor; Zolotykh, Nikolai Yu.; Kozinov, Evgeny; Kustikova, Valentina; Мееров, И.Б.; Половинкин, А. Н.

doi:10.1134/s1054661811020271

Cited by 35 publications

(12 citation statements)

References 7 publications

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“…In order to account for the dynamics of the inner edge of the outer belt, we include the maximum value of the AE over 36 hr as an input parameter. We do not apply the linear regression coefficients to convert the max(AE) to Lpp, as the regression trees are invariant to linear scaling operations (e.g., Druzhkov et al., 2011).…”

Section: Data Setmentioning

confidence: 99%

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

et al. 2020

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The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis. Plain Language Summary The radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise complex and dynamic systems posing a significant threat to a variety of commercial and military satellites. While the inner belt is relatively stable, the outer belt is highly variable and depends substantially on solar activity; therefore, accurate and improved models of electron flux in the outer radiation belt are essential to understand the underlying physical processes. Although many models have been developed for the geostationary orbit and relativistic energies, prediction of electron flux in the 120-600 keV energy range still remains challenging. We present a data-driven model of the medium energies (120-600 keV) differentialelectron flux in the outer radiation belt based on machine learning. We use 17 years of electron observations by Global Positioning System (GPS) satellites. We set up a 3D model for flux prediction in terms of L-values, MLT, and magnetic latitude. The model gives reliable predictions of the radiation environment in the outer radiation belt and has wide space weather applications.

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Section: Data Setmentioning

confidence: 99%

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

et al. 2020

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“…The image data is captured row by row, and the entire image is restored from the individual rows during processing. The question of choosing a matrix or linear camera is related to the scope of the camera and the requirements it must meet [7,8].…”

Section: Methodsmentioning

confidence: 99%

“…Methods of the second class, on the contrary, are based on the grouping of similar vectors from the optical flow together in regions, which are fed to the output of the algorithm as moving objects. Most classical methods do not analyse the video sequence frames themselves but the optic flow (the field of visible displacements of image pixels), built on these frames [5][6][7][8][9][10].…”

Section: Fig 2 Usb Camera Ui-3130cpmentioning

confidence: 99%

“…Typical tasks for such systems are the control of geometric dimensions, product counting, 1D and 2D codes reading, color control, surface inspection, etc. There are different approaches to the realization of technical vision systems, such as comparing the image with the master image or building threedimensional image models according to projections [5,8,9].…”

Section: Introductionmentioning

confidence: 99%

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Automated Control of Technological Parameters of Ceramic Tiles Using Technical Vision

Fedonin¹,

Petreshin²,

Матлахов³

et al. 2019

Proceedings of the International Conference on Aviamechanical Engineering and Transport (AviaENT 2019)

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“…For the training and testing of each individual binary classification detector we utilize an OpenCV C++ interface to LIBSVM [52], [53], [54] using the radial basis function as the kernel.…”

Section: Configurationmentioning

confidence: 99%

Active Authentication Linguistic Modalities

Fridman¹,

Stolerman²,

Acharya³

et al. 2013

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) DECEMBER 2013 REPORT TYPE FINAL TECHNICAL REPORT DATES COVERED (From -To ABSTRACTActive authentication is the process of continuously verifying a user based on their on-going interaction with the computer. In this report, we consider a representative collection of behavioral biometrics: low-level modalities of keystroke dynamics and mouse movement, high-level modalities of stylometry and web browsing behavior. We develop a sensor for each modality and organize the sensors as a parallel binary detection decision fusion architecture. The decisions of each sensor (legitimate/illegitimate user) are fed into a Decision Fusion Center (DFC) which applies the Chair-Varshney fusion algorithm to generate a global decision. The DFC minimizes the probability of error using the local-sensor False Rejection Rates (FAR) and False Acceptance Rates (FRR) as well as the a-priori probability that user is legitimate to form the decision rule. We test our approach on a dataset collected from 67 users, each working individually in an office environment for a period of one week. We show that the fusion algorithm achieves lower probability of error than that of the best individual sensor in the fused set, and we are able to quantify the contribution of each modality to the overall performance. We consider the temporal characteristics of intruder detection, showing results of the time it takes to detect a change in user. We measure the effect of perfect adversarial compromise of sensors as part of the fusion system. Lastly, we consider a higher level classification model of users based on their personality metrics. Active authentication is the process of continuously verifying a user based on their on-going interaction with the computer. In this report, we consider a representative collection of behavioral biometrics: low-level modalities of keystroke dynamics and mouse movement, highlevel modalities of stylometry and web browsing behavior. We develop a sensor for each modality and organize the sensors as a parallel binary detection decision fusion architecture. The decisions of each sensor (legitimate/illeg...

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New object detection features in the OpenCV library

Cited by 35 publications

References 7 publications

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

Automated Control of Technological Parameters of Ceramic Tiles Using Technical Vision

Active Authentication Linguistic Modalities

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