68 research outputs found
Derivative-Based Trapezoid Rule for the Riemann-Stieltjes Integral
The derivative-based trapezoid rule for the Riemann-Stieltjes integral is presented which uses 2 derivative values at the endpoints. This kind of quadrature rule obtains an increase of two orders of precision over the trapezoid rule for the Riemann-Stieltjes integral and the error term is investigated. At last, the rationality of the generalization of derivative-based trapezoid rule for Riemann-Stieltjes integral is demonstrated
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Modern object detectors usually suffer from low accuracy issues, as
foregrounds always drown in tons of backgrounds and become hard examples during
training. Compared with those proposal-based ones, real-time detectors are in
far more serious trouble since they renounce the use of region-proposing stage
which is used to filter a majority of backgrounds for achieving real-time
rates. Though foregrounds as hard examples are in urgent need of being mined
from tons of backgrounds, a considerable number of state-of-the-art real-time
detectors, like YOLO series, have yet to profit from existing hard example
mining methods, as using these methods need detectors fit series of
prerequisites. In this paper, we propose a general hard example mining method
named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for
real-time detectors, as it utilizes the final feature map which exists in all
real-time detectors to mine hard examples. By using LRM, some elements
representing easy examples in final feature map are filtered and detectors are
forced to concentrate on hard examples during training. Extensive experiments
validate the effectiveness of our method. With our method, the improvements of
YOLOv2 detector on auto-driving related dataset KITTI and more general dataset
PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first
hard example mining strategy which could fit YOLOv2 perfectly and make it
better applied in series of real scenarios where both real-time rates and
accurate detection are strongly demanded.Comment: 8 pages, 6 figure
Research on Minimum Safety Distance in Free Flight Based on CNS Performances
Abstract Free flight is one of effective methods to solve airspace congestion in the future. In order to guarantee safety of flight in free flight environment, the minimum safety distance was studied. Within circumstance that collision avoidance system hasn't started to make TCAS logic judgment to flight nearby, communication, navigation and surveillance (CNS) performances play a decisive role to minimum safety distance. The position errors, which were affected by CNS performances, were regarded as Brownian motion along the coordinate direction respectively. Then a model for collision risk in free flight environment was established basing stochastic differential equations. Minimum safety distance between flights can be obtained using dichotomy to optimize under the given Target Level of Safety (TLS). The example shows that the model is feasible
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Characterizing and correcting camera noise in back-illuminated sCMOS cameras
With promising properties of fast imaging speed, large field-of-view, relative low cost and many others, back-illuminated sCMOS cameras have been receiving intensive attention for low light level imaging in the past several years. However, due to the pixel-to-pixel difference of camera noise (called noise non-uniformity) in sCMOS cameras, researchers may hesitate to use them in some application fields, and sometimes wonder whether they should optimize the noise non-uniformity of their sCMOS cameras before using them in a specific application scenario. In this paper, we systematically characterize the impact of different types of sCMOS noise on image quality and perform corrections to these types of sCMOS noise using three representative algorithms (PURE, NCS and MLEsCMOS). We verify that it is possible to use appropriate correction methods to push the non-uniformity of major types of camera noise, including readout noise, offset, and photon response, to a satisfactory level for conventional microscopy and single molecule localization microscopy. We further find out that, after these corrections, global read noise becomes a major concern that limits the imaging performance of back-illuminated sCMOS cameras. We believe this study provides new insights into the understanding of camera noise in back-illuminated sCMOS cameras, and also provides useful information for future development of this promising camera technology.
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