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Multi-Cascade image Correction System for the Large Solar Vacuum Telescope 7

Multi-Cascade Image Correction System for the Large Solar Va...

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conference on Adaptive Optics Systems VII

作者： Lukin, V. P. Botygina, N. N. Antoshkin, L., V Borzilov, A. G. Emaleev, O. N. Konyaev, P. A. Kovadlo, P. G. Kolobov, D. Yu Shikhovtsev, A. Yu Chuprakov, S. A. Russian Acad Sci VE Zuev Inst Atmospher Opt Siberian Branch Tomsk 634055 Russia Russian Acad Sci Inst Solar Terr Phys Siberian Branch Irkutsk 664033 Russia

ISBN: (纸本)9781510636842

A multi-cascade adaptive optical system for imaging and image stabilization for the Large Solar Vacuum Telescope is described. This system was created in 2017 by specialists of the V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, with the technical support of the Institute of Solar-Terrestrial Physics SB RAS, Irkutsk. The system has been tested at the Large Solar Vacuum Telescope (Baikal Astrophysical Observatory) and demonstrated its efficiency. Along with the first cascade of adaptive image stabilization by a tip-tilt corrected mirror, this system employs the second imaging cascade based on correction with a flexible mirror controlled by a specialized wavefront sensor, as well as the third cascade for real-time post-detector processing of video camera frames. Reliable experimental data confirming the efficiency of the multi-cascade adaptive system for image formation and stabilization have been obtained. Three high-rate digital video cameras recording simultaneously digital images with rates from 300 to 980 frames per second were used to test the system. The mirror correcting wavefront tilts and operating in a closed optical feedback loop was controlled by the specially developed software including the fast correlation tracking algorithm. The post-detector digital imaging was performed with a special software for processing of video camera frames in real time with the use of modern high-speed parallel algorithms based on the Intel MKL and IPP libraries.

关键词： astronomy solar telescope astroclimate turbulence adaptive optics tilt corrector correction cascade

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A Salient Object Detection Technique Based on Color Divergence 20

A Salient Object Detection Technique Based on Color Divergen...

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The 4th International conference on Future Networks and distributed Systems (ICFNDS)

作者： Sana Sahar Guia Abdelkader Laouid Reinhardt Euler Mohammed Amine Yagoub Ahcene Bounceur Mohammad Hammoudeh El Oued University University of Western Brittany UBO Manchester Metropolitan University

ISBN: (纸本)9781450388863

Nowadays, multimedia and visual computing advances in digital technology make a potential change in human life. Many applications exploit the captured images from autonomous entities as data sources for several goals. In fact, these captured images need to be interpreted in order to extract their external environment. The researchers of this domain will meet some challenges such as how to detect and interpret the images’ context. This paper is to propose an efficient technique that detects objects of a given image based on the color divergence. The results clearly show the accuracy and the computation speed of the proposed technique compared with other methods.

关键词： image processing Data Analysis. Object Detection

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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System

Classification Of Human Facial Expressions For Emotion Recog...

引用

International conference on Computer Science and Engineering (UBMK)

作者： Timuçin Korkmaz Hamza Erol Department of Computer Engineering Mersin University Mersin Turkey

ISBN: (数字)9781728175652

ISBN: (纸本)9781728175669

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image flies were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed are taken into account; and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Face recognition distributed databases Computer architecture IP networks Servers Emotion recognition

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Cyber Attacks Detection Based on Generative Adversarial Networks

Cyber Attacks Detection Based on Generative Adversarial Netw...

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Computers and Communications (ACCC), Asia conference on

作者： Aining Shi Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665407441

The development of the Internet of things (IoT) and other modern technologies brings not only great convenience and intelligence to society but also the severe problem of cyber security. In recent years, a large number of network attacks, especially botnet and distributed denial of service (DDoS) attacks which are difficult to detect, have seriously affected the normal operation of social work. Solutions and methods based on artificial intelligence techniques are therefore proposed to help detect cyberattacks. In this paper, the application of GAN in the field of network security is discussed. GAN has been one of the most significant deep learning models in recent years. It consists of a generative model and a discriminative model which can be combined to produce a dynamic game system. GAN has been applied in various fields such as image generation, speech processing, data enhancement, and cyberattack detection. This paper demonstrates the basic working principle and infrastructure of GAN, and also focuses on how the GAN model assists with cyber intrusion detection.

关键词： Deep learning image synthesis Intrusion detection Games Network security Generative adversarial networks Denial-of-service attack

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Effect of parallel processing by duplicating histogram in automatic image binarization for high-level synthesis 20

Effect of parallel processing by duplicating histogram in au...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Yamasaki, Moena Yamawaki, Akira Department of Electrical Engineering and Electronics Kyushu Institute of Technology Kitakyushu Japan

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

The high-level synthesis converting software to hardware automatically is one of the important technologies for significantly reducing the burden caused by developing hardware module. The embedded image processing products need the HLS to quickly implement a hardware module achieving high-performance with low-power consumption instead of software implementation for high-computational processing. However, the HLS tool cannot generate a hardware module with high-performance and low-power consumption expected when the software program without deep consideration about the hardware organization to be generated is input. This paper shows a software description method to extract some parallelisms by duplicating the histograms in the automatic image binarization which is called as Otsu's method in order to improve a performance of the hardware module HLS generates. The experimental results show the effect of our description method about the performance and the amount of hardware. We also discuss about a trade-off among the parallelism, performance and amount of hardware. © 2019 IEEE.

关键词： Economic and social effects

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Learning Hierarchical Graph for Occluded Pedestrian Detection 20

Learning Hierarchical Graph for Occluded Pedestrian Detectio...

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28th ACM International conference on Multimedia (MM)

作者： Li, Gang Li, Jian Zhang, Shanshan Yang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781450379885

Although pedestrian detection has made significant progress with the help of deep convolution neural networks, it is still a challenging problem to detect occluded pedestrians since the occluded ones can not provide sufficient information for classification and regression. In this paper, we propose a novel Hierarchical Graph Pedestrian Detector (HGPD), which integrates semantic and spatial relation information to construct two graphs named intra-proposal graph and inter-proposal graph, without relying on extra cues w.r.t visible regions. In order to capture the occlusion patterns and enhance features from visible regions, the intra-proposal graph considers body parts as nodes and assigns corresponding edge weights based on semantic relations between body parts. On the other hand, the inter-proposal graph adopts spatial relations between neighbouring proposals to provide additional proposal-wise context information for each proposal, which alleviates the lack of information caused by occlusion. We conduct extensive experiments on standard benchmarks of CityPersons and Caltech to demonstrate the effectiveness of our method. On CityPersons, our approach outperforms the baseline method by a large margin of 5.24pp on the heavy occlusion set, and surpasses all previous methods;on Caltech, we establish a new state of the art of 3.78% MR. Code is available at https://***/ligang-cs/PedestrianDetection-HGPD.

关键词： computer vision object detection occluded pedestrian detection graph neural networks

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Accelerating Deep Learning Tasks with Optimized GPU-assisted image Decoding

Accelerating Deep Learning Tasks with Optimized GPU-assisted...

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International conference on parallel and distributed Systems (ICPADS)

作者： Lipeng Wang Qiong Luo Shengen Yan HKUST Hong Kong China SenseTime Research Shenzhen China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

In computer vision deep learning (DL) tasks, most of the input image datasets are stored in the JPEG format. These JPEG datasets need to be decoded before DL tasks are performed on them. We observe two problems in the current JPEG decoding procedures for DL tasks: (1) the decoding of image entropy data in the decoder is performed sequentially, and this sequential decoding repeats with the DL iterations, which takes significant time; (2) Current parallel decoding methods under-utilize the massive hardware threads on GPUs. To reduce the image decoding time, we introduce a pre-scan mechanism to avoid the repeated image scanning in DL tasks. Our pre-scan generates boundary markers for entropy data so that the decoding can be performed in parallel. To cooperate with the existing dataset storage and caching systems, we propose two modes of the pre-scan mechanism: a compatible mode and a fast mode. The compatible mode does not change the image file structure so pre-scanned files can be stored back to disk for subsequent DL tasks. In comparison, the fast mode crafts a JPEG image into a binary format suitable for parallel decoding, which can be processed directly on the GPU. Since the GPU has thousands of hardware threads, we propose a fine-grained parallel decoding method on the pre-scanned dataset. The fine-grained parallelism utilizes the GPU effectively, and achieves speedups of around 1.5× over existing GPU-assisted image decoding libraries on real-world DL tasks.

关键词： Deep learning Transform coding Graphics processing units Hardware Decoding Task analysis Iterative decoding

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Underwater image Enhancement with a Total Generalized Variation Illumination Prior 17

Underwater Image Enhancement with a Total Generalized Variat...

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IEEE Int Conf on parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Zhao, Zhengjie Dai, Yuxiang Zhuang, Peixian Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

ISBN: (纸本)9781728143286

In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.

关键词： Underwater image enhancement Total generalized variation Retinex Alternating optimization

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration 6

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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6th IEEE International conference on Computer and Communications, ICCC 2020

作者： Zhang, Zhe Liu, Jie Chi, Lihua Chen, Xinhai Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728186351

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%. © 2020 IEEE.

关键词： Recurrent neural networks

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Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty 34

Stochastic segmentation networks: Modelling spatially correl...

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34th conference on Neural Information processing Systems, NeurIPS 2020

作者： Monteiro, Miguel Le Folgoc, Loïc de Castro, Daniel Coelho Pawlowski, Nick Marques, Bernardo Kamnitsas, Konstantinos van der Wilk, Mark Glocker, Ben Imperial College London United Kingdom

In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of object boundaries. Estimating this inherent uncertainty and predicting multiple plausible hypotheses is of great interest in many applications, yet this ability is lacking in most current deep learning methods. In this paper, we introduce stochastic segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. In contrast to approaches that produce pixel-wise estimates, SSNs model joint distributions over entire label maps and thus can generate multiple spatially coherent hypotheses for a single image. By using a low-rank multivariate normal distribution over the logit space to model the probability of the label map given the image, we obtain a spatially consistent probability distribution that can be efficiently computed by a neural network without any changes to the underlying architecture. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans. SSNs outperform state-of-the-art for modelling correlated uncertainty in ambiguous images while being much simpler, more flexible, and more efficient. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： image segmentation

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