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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Guojin Peng Meini Lv Kuikui Wang Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University WuZhou china

ISBN: (纸本)9781728152103

Curves generated by traditional evaluating functions contain large amount of local extremums, easily leading to focal-plane misjudgments. We propose a new clarity evaluation method based on spectral radius. The spatial information is converted into frequency domain by Discrete Cosine Transformation, then a sector that contains more than 99 percent of the total spectrum energy is extracted, and the radius of this sector is employed to estimate the degree of clarity of current image. Since the scope of spectral radius is steady and limited to image size, it provides effective reference for selection of lower limit of spectral radius which is utilized to avoid local fluctuation and maintain the unimodality of evaluation curves. The experimental results demonstrate that the spectral radius based evaluation method forms curves with fewer local extremums and better robustness.

关键词： discrete cosine transforms gradient methods image recognition spectral radius discrete cosine transforms gradient methods image recognition Curves Clarity Current Pick image SIZE Sectoring SPECTRAL ENERGY evaluation method

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Scalable deep learning on distributed infrastructures: Challenges, techniques and tools

arXiv

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arXiv 2019年

作者： MAYER, RUBEN JACOBSEN, HANS-ARNO Technical University of Munich Boltzmannstr. 3 Garching85748 Germany

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-art results in various domains such as image recognition and natural language processing. One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available. To keep on improving the performance of DL, increasing the scalability of DL systems is necessary. In this survey, we perform a broad and thorough investigation on challenges, techniques and tools for scalable DL on distributed infrastructures. This incorporates infrastructures for DL, methods for parallel DL training, multitenant resource scheduling and the management of training and model data. Further, we analyze and compare 11 current open-source DL frameworks and tools and investigate which of the techniques are commonly implemented in practice. Finally, we highlight future research trends in DL systems that deserve further research. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY 25

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

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25th IEEE International Conference on image processing (ICIP)

作者： Qin, Zheng Zhang, Zhaoning Chen, Xiaotao Wang, Changjian Peng, Yuxing Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast downsampling strategy to MobileNet framework. In FD-MobileNet, we perform 32x downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11x inference speedup over MobileNet and 1.82x over ShuffleNet under the same complexity.

关键词： Computer vision convolutional neural network deep learning

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A Tone-Mapping Technique Based on Histogram Using a Sensitivity Model of the Human Visual System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第4期65卷 3469-3479页

作者： Khan, Ishtiaq Rasool Rahardja, Susanto Khan, Muhammad Murtaza Movania, Muhammad Mobeen Abed, Fidaa King Abdulaziz Univ Fac Comp & IT Jeddah Saudi Arabia Univ Jeddah Fac Comp & IT Jeddah 21589 Saudi Arabia Northwestern Polytech Univ Xian 710072 Shaanxi Peoples R China DHA Suffa Univ Karachi 710072 Pakistan

High-dynamic-range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. In this paper, a tone-mapping algorithm that uses histogram of luminance to construct a lookup table (LUT) for tone mapping is presented. Characteristics of the human visual system (HVS) are used to give more importance to visually distinguishable intensities while constructing the histogram bins. The method begins with constructing a histogram of the luminance channel, using bins that are perceived to be uniformly spaced by the HVS. Next, a refinement step is used, which removes the pixels from the bins that are indistinguishable by the HVS. Finally, the available display levels are distributed among the bins proportionate to the pixels counts thus giving due consideration to the visual contribution of each bin in the image. Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods. Finally, implementation details of the algorithm on GPU for parallel processing are presented, which could achieve a significant gain in speed over CPU-based implementation.

关键词： High dynamic range (HDR) human visual system (HVS) image enhancement tone mapping

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Semi-Supervised Generative Adversarial Model for 3D Recognition

Semi-Supervised Generative Adversarial Model for 3D Recognit...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Haoqian Wang Shuhao Zhang Yongbing Zhang Zhihong Liu Key Laboratory of Broadband Network & Multimedia Graduate School at Shenzhen Tsinghua University Shenzhen China Shenzhen Institute of Future Technology Shenzhen China

ISBN: (纸本)9781728152103

In this paper, we focus on the issue of 3D point cloud recognition utilizing semi-supervised generative adversarial network, composed of two parts: classification net and generative adversarial net. Using the adversarial criterion, the generator learns to establish the mapping relationship from one-dimensional noise data to 3D point cloud, which can remedy the insufficient data available faced by traditional supervised methods, and the adversarial discriminator acts as a powerful 3D object descriptor to get the point cloud into the right category and eliminate the fake data brought by the generator. In order to fix the rotation problem of point cloud data and improve the recognition performance of the model, we also introduce the spatial transformation networks into the discriminator. Compared with the current supervised and unsupervised methods, the impressive experimental results we show illustrate the effectiveness of the proposed method.

关键词： image classification neural nets object recognition stereo image processing supervised learning Stereo image processing Neural network Semi-supervised Learning image classification object recognition Clouds dynamos Recognition (Psychology)

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Spatial relationship between the field-measured ambient gamma dose equivalent rate and geological conditions in a granitic area, Velence Hills, Hungary: An application of digital spatial analysis methods

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JOURNAL OF ENVIRONMENTAL RADIOACTIVITY 2018年第Dec.期192卷 267-278页

作者： Torres, Silvana Beltran Petrik, Attila Szabo, Katalin Zsuzsanna Jordan, Gyozo Yao, Jun Szabo, Csaba Eotvos Lorand Univ Dept Petrol & Geochem Lithosphere Fluid Res Lab Pazmany Peter Setany 1-C H-1117 Budapest Hungary Univ Naples Federico II Dept Earth Environm & Resources Sci I-80126 Naples Italy Szent Istvan Univ Dept Chem Pater Karoly Utca 1 H-2103 Godollo Hungary Szent Istvan Univ Dept Appl Chem Villanyi Ut 35-43 H-1118 Budapest Hungary Chinese Acad Sci State Key Lab Environm Geochem 99 Linchengxi Rd Guiyang 550081 Guizhou Peoples R China China Univ Geosci Beijing Inst Earth Sci 29 Xueyuan Rd Beijing Peoples R China

In order to estimate the annual dose that the public receive from natural radioactivity, the identification of the potential risk areas is required which, in turn, necessitates understanding the relationship between the spatial distribution of natural radioactivity and the geogenic risk factors (e.g., rock types, presence of dikes, faults, physical conditions of soil, etc.). A detailed spatial analysis of outdoor ambient gamma dose equivalent rate was performed in the western side of Velence Hills, the largest outcropped granitic area in Hungary. In order to assess the role of local geology in the spatial distribution of gamma dose rates, field measurements were carried out at ground level at 300 sites along a 250 m x 250 m regular grid in a total surface of 19.8 km(2). Digital image processing methods were applied to identify anomalies, heterogeneities and spatial patterns in the measured gamma dose rates, including local maxima and minima determination, digital cross sections, gradient magnitude and gradient direction, second derivative profile curvature, local variability, lineament density, 2D auto correlation and directional variogram analyses. Statistical inference shows that different gamma dose rate levels are associated with the geological formations, with the highest level on the Carboniferous granite including outlying values. Moreover, digital image processing reveales that linear gamma dose rate spatial features are parallel to the SW-NE dike system and to the NW-SE main fractures. The results of this study underline the importance of understanding the role of geogenic risk factors influencing the ambient gamma dose equivalent rate received by public. The study also demonstrates the power of the image processing techniques for the identification of spatial pattern in field-measured geogenic radiation.

关键词： Digital image processing GIS Geochemical mapping Spatial modelling

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Graph Convolutional Networks with Objects for Skeleton-Based Action Recognition

Graph Convolutional Networks with Objects for Skeleton-Based...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiangbin Shi Haowen Li Fang Liu Deyuan Zhang Jing Bi Zhaokui Li School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Recently, graph convolutional neural networks has become a research hotspot for skeleton-based action recognition because of its excellent performance on graph structure data. Compared to traditional methods, it can explicitly exploit the natural connectivity among the joints and improve greater expressive power. In this paper, we propose a two-stream graph convolutional networks with objects for skeleton-based action recognition. An algorithm is designed for matching similar skeleton in adjacent frames, so that we can get the right skeletons which belong to the same person. It performs well when there are other irrelevant persons in the scene. In addition, other features are less employed except for the human joint in skeleton-based action recognition. We introduce limbs orientation information and related objects information. The related objects are treated as joint points which link with hands. The two-stream networks are built to model coordinate features and orientation features respectively, the results of two streams are fused to one. We get good results on the Kinetics dataset with our methods.

关键词： convolutional neural nets feature extraction graph theory image motion analysis image recognition image representation learning (artificial intelligence) object recognition video signal processing image motion analysis Video signal processing image representation image recognition Graph theory object recognition Feature extraction Line graph Recognition (Psychology) Joints

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Auto-Resource Provisioning for MapReduce-Based Multiple Object Tracking in Video 18

Auto-Resource Provisioning for MapReduce-Based Multiple Obje...

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19th International Conference on distributed Computing and Networking (ICDCN)

作者： Singh, Gurinderbeer Majumdar, Shikharesh Rajan, Sreeraman Carleton Univ Syst & Comp Engn Dept Ottawa ON Canada

ISBN: (纸本)9781450363723

Use of complex image analysis and globally optimal techniques make the current Multiple Object Tracking (MOT) methods for video analysis computationally slow. An important issue in this context is meeting the specific latency requirement for a given application while processing large scale video data. This is especially important in emergency situations such as accidents, natural calamities, and terrorist attacks. This paper introduces a latency reducing MapReduce/Hadoop-based parallel solution for MOT. The system includes an Auto-Resource Provisioning technique for determining the number of Hadoop nodes required to process the MOT job within a user specified deadline. The estimated number of nodes are then provisioned by the system and the MOT application is executed on the Hadoop cluster comprising the desired number of nodes. A prototype is built using the AWS EC2 cloud. A performance analysis is performed using measurements made on the prototype and insights gained into system behavior and performance are presented.

关键词： Video processing Multiple Object Tracking Cloud Computing distributed Systems MapReduce Apache Hadoop

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DP-Pose: Multi-Person Pose Estimation in Video Sequence through Dynamic Programming

DP-Pose: Multi-Person Pose Estimation in Video Sequence thro...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Deyuan Zhang Junyuan Wang Xiangbin Shi Zhaokui Li Fang Liu Cuiwei Liu School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Human pose estimation is an important research topic in the field of computer vision. Bottom-up methods such as OpenPose have become prevalent pose estimation methods because of its high efficiency and detection speed. But for poor quality video, the keypoints obtained by the bottom-up method have suffered from jitter and loss. In this paper, we propose DP-Pose, which constructs candidate pose sequence by selecting points in a region of the heatmap, improves constraint function by combining distance and confidence, and solves the optimal keypoints location by dynamic programming. The experimental results on class dataset show that DP-Pose can recover missing keypoint using heatmap data and obtain stable pose from candidate pose sequences.

关键词： computer vision dynamic programming image sequences pose estimation video signal processing pose estimation dynamic programming Video signal processing images computer vision image sequences Video sequences candidate species

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Scaling handwritten student assessments with a document image workflow system 31

Scaling handwritten student assessments with a document imag...

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31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

作者： Rowtula, Vijay Bhargavan, Varun Kumar, Mohan Jawahar, C.V. IIIT Hyderabad India

With the increase in the number of students enrolled in the university system, regular assessment of student performance has become challenging. This is specially true in case of summative assessments, where one expects the student to write down an answer on paper, rather than selecting a correct answer from multiple choices. In this paper, we present a document image workflow system that helps in scaling the handwritten student assessments in a typical university setting. We argue that this improves the efficiency since the book keeping time as well as physical paper movement is minimized. An electronic workflow can make the anonymization easy, alleviating the fear of biases in many cases. Also, parallel and distributed assessment by multiple instructors is straightforward in an electronic workflow system. At the heart of our solution, we have (i) a distributed image capture module with a mobile phone (ii) image processing algorithms that improve the quality and readability (iii) image annotation module that process the evaluations/feedbacks as a separate layer. Our system also acts as a platform for modern image analysis which can be adapted to the domain of student assessments. This include (i) Handwriting recognition and word spotting [5] (ii) Measure of document similarity [6] (iii) Aesthetic analysis of handwriting [7] (iv) Identity of the writer [4] etc. With the handwriting assessment workflow system, all these recent advances in computer vision can become practical and applicable in evaluating student assessments. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Students

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