Recently, deep convolutional neural networks (DCNNs) have set a new trend in the computervision community by improving the state-of-the-art performance in almost all of applications. We propose DCNN-based face recogn...
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Recently, deep convolutional neural networks (DCNNs) have set a new trend in the computervision community by improving the state-of-the-art performance in almost all of applications. We propose DCNN-based face recognition algorithm. This paper aims at analyzing and verifying considerations when the proposed method is implemented in a real environment. First, Multiple images of the same scene are processed. Also, this novel method is considered together with a method to reduce the total recognition time in the perspective of integrated system. We analyzed the experimental data and evaluated the performance by setting up the sensor fusion network in actual classroom.
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at m...
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ISBN:
(数字)9781728150239
ISBN:
(纸本)9781728150246
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computervision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.
Microscopy imaging plays a vital role in understanding many biological processes in development and disease. The recent advances in automation of microscopes and development of methods and markers for live cell imagin...
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The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. ...
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The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open source code gSeaGen, which allows the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionality but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments. Program summary: Program title: gSeaGen CPC Library link to program files: https://***/10.17632/ymgxvy2br4.2 Developer's respository link: ***/opensource/gseagen Licensing provisions: BSD 3-Clause Programming language: C++ Nature of problem: Integration of the state-of-the-art extensive air shower Monte Carlo event generator CORSIKA [1] into the atmospheric muon simulation for water Cherenkov neutrino telescopes. The primary use case considered is the KM3NeT experiment [2], however, the code should be able to cover other similar experiments as well. The challenges in this work included interfacing the CORSIKA binary output, efficient handling of already generated events to reduce the overall computational cost, and preserving all the additional available information, which can be invaluable in physics analyses. Solution method: The readout of CORSIKA simulation was adapted from the base script provided together with CORSIKA and implemented as a standalone flux driver in gSeaGen. The propagation routine has been redesigned to support the geometry of extensive air shower simulations and to improve its efficiency in propagating particles to the detector. To ensure a reliable modelling of muon energy loss and scattering, PROPOSAL [3] was set as the default internal code for m
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organi...
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Lorentz invariance is a fundamental symmetry of spacetime and foundational to modern physics. One of its most important consequences is the constancy of the speed of light. This invariance, together with the geometry ...
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On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in lig...
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Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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Correlation filters have attracted growing attention due to their high efficiency, which have been well studied for binary classification. However, by setting the desired output to be a fixed Gaussian function, the co...
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The KM3NeT experiment reported the detection of an ultra-high-energy neutrino with an energy estimate of ∼ 220 PeV, the most energetic yet observed. The neutrino arrival direction has a 99% confidence region of 3◦ ra...
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