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18th IEEE International conference on High Performance Computing and Communications (HPCC) / 14th IEEE International conference on Smart City (Smart City) / 2nd IEEE International conference on Data Science and systems (DSS)

作者： Zhao, Meiting Liu, Rui Liu, Yi Song, Kaida Qian, Depei Beihang Univ Sinogerman Joint Software Inst Beijing Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Peoples R China

ISBN: (纸本)9781509042975

Many image processing algorithms have been parallelized successfully on many-core processors, such as GPU and Intel Xeon Phi. In this paper, we choose the Sunway many-core processor SW26010, which is a new processor designed and made in China that constitutes the current NO. 1 supercomputer Sunway TaihuLight. This paper firstly introduces the architecture of Sunway SW26010 processor and two representative image processing algorithms: local binary pattern (LBP) and histogram of oriented gradient (HOG). Furthermore we propose a method of parallel implementation, and the experimental results of this method show that the speedup can be up to 170 for LBP and 33 for HOG. Then two optimized methods are brought forward based on this parallel implementation, including the optimization of program and parallel design. We optimize the program by using the method that combined step transmission and software prefetching. From the experiment results of the first optimization we can know that the maximum speedup can reach 310 for LBP with processing high-resolution images and 83 for HOG. Then we optimize the parallel design by using a coarse grained parallel method, and the experimental results show that the speedup can be up to 370 for LBP and 95 for HOG when processing low-resolution images. Finally, we investigate the scalability of our parallelism on the Sunway TaihuLight with different number of processor nodes, and the experiment results prove that the two algorithms' parallel design and implementation have better expansibility.

关键词： image processing Sunway SW26010 processor the Sunway TaihuLight supercomputer parallel implementation optimization

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Impact of interactivity on the assessment of quality of experience for light field content

Impact of interactivity on the assessment of quality of expe...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Irene Viola Martin Řeřábek Touradj Ebrahimi EPFL Multimedia Signal Processing Group Lausanne Switzerland

The recent advances in light field imaging are changing the way in which visual content is captured, processed and consumed. Storage and delivery systems for light field images rely on efficient compression algorithms. Such algorithms must additionally take into account the feature-rich rendering for light field content. Therefore, a proper evaluation of visual quality is essential to design and improve coding solutions for light field content. Consequently, the design of subjective tests should also reflect the light field rendering process. This paper aims at presenting and comparing two methodologies to assess the quality of experience in light field imaging. The first methodology uses an interactive approach, allowing subjects to engage with the light field content when assessing it. The second, on the other hand, is completely passive to ensure all the subjects will have the same experience. Advantages and drawbacks of each approach are compared by relying on statistical analysis of results and conclusions are drawn. The obtained results provide useful insights for future design of evaluation techniques for light field content.

关键词： image coding Visualization Imaging image resolution image color analysis Software Temperature measurement

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Learning and recognizing human action from Skeleton movement with deep residual neural networks

Learning and recognizing human action from Skeleton movement...

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8th International conference of Pattern Recognition systems (ICPRS 2017)

作者： Huy-Hieu Pham Louahdi Khoudour Alain Crouzil Pablo Zegers Sergio A. Velastin Centre d'Etudes et d'Expertise sur les Risques l'Environnement la Mobilité et l'Aménagement France Universite Toulouse III Paul Sabatier Toulouse Occitanie FR Universidad de los Andes Las Condes Santiago CL Universidad Carlos III de Madrid Madrid Madrid ES

ISBN: (纸本)9781785616525

Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a lot of progresses, recognizing actions in a unknown video is still a challenging task in computer vision. Recently, deep learning algorithms has proved its great potential in many vision-related recognition tasks. In this paper, we propose the use of Deep Residual Neural Networks (ResNets) to learn and recognize human action from skeleton data provided by Kinect sensor. Firstly, the body joint coordinates are transformed into 3D-arrays and saved in RGB images space. Five different deep learning models based on ResNet have been designed to extract image features and classify them into classes. Experiments are conducted on two public video datasets for human action recognition containing various challenges. The results show that our method achieves the state-of-the-art performance comparing with existing approaches.

关键词： Action recognition ResNet Skeleton Kinect

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Comparison of Turbulence Mitigation algorithms

Comparison of Turbulence Mitigation Algorithms

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conference on Long-Range Imaging

作者： Kozacik, Stephen Paolini, Aaron Bonnett, James Kelmelis, Eric EM Photon 51 East Main StSuite 203 Newark DE 19711 USA

ISBN: (纸本)9781510600874

When capturing image data over long distances (0.5 km and above), images are often degraded by atmospheric turbulence, especially when imaging paths are close to the ground or in hot environments. These issues manifest as time-varying scintillation and warping effects that decrease the effective resolution of the sensor and reduce actionable intelligence. In recent years, several image processing approaches to turbulence mitigation have shown promise. Each of these algorithms have different computational requirements, usability demands, and degrees of independence from camera sensors. They also produce different degrees of enhancement when applied to turbulent imagery. Additionally, some of these algorithms are applicable to real-time operational scenarios while others may only be suitable for post-processing workflows. EM Photonics has been developing image-processing-based turbulence mitigation technology since 2005 as a part of our ATCOM [1] image processing suite. In this paper we will compare techniques from the literature with our commercially-available real-time GPU accelerated turbulence mitigation software suite, as well as in-house research algorithms. These comparisons will be made using real, experimentally-obtained data for a variety of different conditions, including varying optical hardware, imaging range, subjects, and turbulence conditions. Comparison metrics will include image quality, video latency, computational complexity, and potential for real-time operation.

关键词： image fusion Turbulence Reconstruction algorithms Speckle Deconvolution image processing Cameras Imaging systems image quality Photonics

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Edge detection based on Retinex theory and wavelet multiscale product for mine images

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APPLIED OPTICS 2016年第34期55卷 9625-9637页

作者： Du, Yuxin Tong, Minming Zhou, Lingling Dong, Haibo China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221008 Peoples R China

The application of visual technology to mine robots has become a hot topic in the development of coal mine automatic production. Key techniques of robot control are the feature recognition of sampled videos and the perception of complex surroundings. However, it is difficult for features in underground images with dark hue and low target discrimination to be recognized and extracted, especially for reasons of the nonuniform illumination and heavy dust concentration in mines. Hence, an edge detection algorithm based on the Retinex theory and wavelet multiscale product is proposed in this paper for low-light-level mine image feature extraction, which employs a modified multiscale Retinex method to deal with the low frequency subplot after the wavelet decomposition, an improved fuzzy enhancement approach to handle high frequency components, and finally a revised multiscale product edge detection algorithm to obtain the ultima edge image. Compared with a variety of algorithms by detecting edges of both normal illuminated and underground images, experimental results show that with characteristics of high real-time performance and detection accuracy, the proposed algorithm can exactly meet the needs of surrounding environment perception for mine robots, which applies well to image edge detection in low illumination mines. (C) 2016 Optical Society of America

关键词： Digital image processing Edge detection Feature extraction Fourier transforms image processing Optical control systems

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BACKGROUND PRIOR AND BOUNDARY WEIGHT-BASED PEDESTRIAN SEGMENTATION IN INFRARED imageS 23

BACKGROUND PRIOR AND BOUNDARY WEIGHT-BASED PEDESTRIAN SEGMEN...

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23rd IEEE International conference on image processing (ICIP)

作者： Li, Lu Zheng, Yu Bai, Xiangzhi Zhou, Fugen Beihang Univ Image Proc Ctr Beijing 100191 Peoples R China

ISBN: (纸本)9781467399616

Pedestrian segmentation in infrared images is a difficult problem for the defects of low SNR and inhomogeneous luminance distribution. In this paper, we propose a method which aims to obtain the accurate pedestrian segmentation through a background prior and boundary weight-based saliency. Background likelihood is firstly calculated as background prior to get an abstract representation for infrared pedestrian. Then, by considering the object-center prior, the object-biased Gaussian model is applied to derive the probability density estimation for pedestrians. Finally, the above two results are integrated with the boundary weight to obtain the final saliency map for infrared image, based on which pedestrians can be easily segmented. Experimental results on real infrared images captured by intelligent transportation systems demonstrate the effectiveness of the proposed approach against the state-of-the-art algorithms.

关键词： infrared image saliency pedestrian segmentation background prior

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An Embedded Solution for Multispectral Palmprint Recognition

An Embedded Solution for Multispectral Palmprint Recognition

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European Signal processing conference

作者： Chao LI Yannick BENEZETH Keisuke NAKAMURA Randy GOMEZ Fan YANG LE2I UMR6306 CNRS Univ. Bourgogne Franche-Comte Dijon France Chinese Academy of Sciences Institute of Acoustics Beijing Chine Honda Research Institute Japan Japan

Palmprint based identification has attracted much attention in the past decades. In some real-life applications, portable personal authentication systems with high accuracy and speed efficiency are required. This paper presents an embedded palmprint recognition solution based on the multispectral image modality. We first develop an effective recognition algorithm by using partial least squares regression, then a FPGA prototype is implemented and optimized through high-level synthesis technique. The evaluation experiments demonstrate that the proposed system can achieve a higher recognition rate at a lower running cost comparing to the reference implementations.

关键词： Signal processing algorithms Optimization Algorithm design and analysis Feature extraction Databases Pipelines

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Automatic Safety Helmet Wearing Detection

Automatic Safety Helmet Wearing Detection

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Kang Li Xiaoguang Zhao Jiang Bian Min Tan The State Key Laboratory of Management and Control for Complex System University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538604915

Surveillance is very essential for the safety of power substation. The detection of whether wearing safety helmets or not for perambulatory workers is the key component of overall intelligent surveillance system in power substation. In this paper, a novel and practical safety helmet detection framework based on computer vision, machine learning and image processing is proposed. In order to ascertain motion objects in power substation, the ViBe background modelling algorithm is employed. Moreover, based on the result of motion objects segmentation, real-time human classification framework C4 is applied to locate pedestrian in power substation accurately and quickly. Finally, according to the result of pedestrian detection, the safety helmet wearing detection is implemented using the head location, the color space transformation and the color feature discrimination. Extensive compelling experimental results in power substation illustrate the efficiency and effectiveness of the proposed framework.

关键词： Safety Substations Feature extraction image color analysis Support vector machines Surveillance Classification algorithms

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The Evolutionary Process of image Transition in Conjunction with Box and Strip Mutation

The Evolutionary Process of Image Transition in Conjunction ...

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23rd International conference on Neural Information processing (ICONIP)

作者： Neumann, Aneta Alexander, Bradley Neumann, Frank Univ Adelaide Sch Comp Sci Optimisat & Logist Adelaide SA Australia

ISBN: (纸本)9783319466750;9783319466743

Evolutionary algorithms have been used in many ways to generate digital art. We study how evolutionary processes are used for evolutionary art and present a new approach to the transition of images. Our main idea is to define evolutionary processes for digital image transition, combining different variants of mutation and evolutionary mechanisms. We introduce box and strip mutation operators which are specifically designed for image transition. Our experimental results show that the process of an evolutionary algorithm in combination with these mutation operators can be used as a valuable way to produce unique generative art.

关键词： Evolutionary algorithms

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Learned D-AMP: principled neural network based compressive image recovery 17

Learned D-AMP: principled neural network based compressive i...

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Proceedings of the 31st International conference on Neural Information processing systems

作者： Christopher A. Metzler Ali Mousavi Richard G. Baraniuk Rice University

ISBN: (纸本)9781510860964

Compressive image recovery is a challenging problem that requires fast and accurate algorithms. Recently, neural networks have been applied to this problem with promising results. By exploiting massively parallel GPU processing architectures and oodles of training data, they can run orders of magnitude faster than existing techniques. However, these methods are largely unprincipled black boxes that are difficult to train and often-times specific to a single measurement *** was recently demonstrated that iterative sparse-signal-recovery algorithms can be "unrolled" to form interpretable deep networks. Taking inspiration from this work, we develop a novel neural network architecture that mimics the behavior of the denoising-based approximate message passing (D-AMP) algorithm. We call this new network Learned D-AMP (LDAMP).The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance. Most importantly, it outperforms the state-of-the-art BM3D-AMP and NLR-CS algorithms in terms of both accuracy and run time. At high resolutions, and when used with sensing matrices that have fast implementations, LDAMP runs over 50 x faster than BM3D-AMP and hundreds of times faster than NLR-CS.

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