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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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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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A Memory Efficient parallel Particle-Based Volume Rendering for Large-Scale distributed Unstructured Volume Datasets in HPC Environments 18th

A Memory Efficient Parallel Particle-Based Volume Rendering ...

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18th Annual Asia Simulation conference (AsiaSim)

作者： Yamaoka, Yoshiaki Hayashi, Kengo Sakamoto, Naohisa Nonaka, Jorji Kobe Univ Kobe Hyogo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9789811328534;9789811328527

In recent years, the size and complexity of the datasets generated by the large-scale numerical simulations using modern HPC (High Performance Computing) systems have continuously increasing. These generated datasets can possess different formats, types, and attributes. In this work, we have focused on the large-scale distributed unstructured volume datasets, which are still applied on numerical simulations in a variety of scientific and engineering fields. Although volume rendering is one of the most popular techniques for analyzing and exploring a given volume data, in the case of unstructured volume data, the time-consuming visibility sorting becomes problematic as the data size increases. Focusing on an effective volume rendering of large-scale distributed unstructured volume datasets generated in HPC environments, we opted for using the well-known PBVR (Particle-based Volume Rendering) method. Although PBVR does not require any visibility sorting during the rendering process, the CPU-based approach has a notorious image quality and memory consumption tradeoff. This is because that the entire set of the intermediate rendering primitives (particles) was required to be stored a priori to the rendering processing. In order to minimize the high pressure on the memory consumption, we propose a fully parallel PBVR approach, which eliminates the necessity for storing these intermediate rendering primitives, as required by the existing approaches. In the proposed method, each set of the rendering primitives is directly converted to a partial image by the processes, and then they are gathered and merged by the utilized parallel image composition library (234Compositor). We evaluated the memory cost and processing time by using a real CFD simulation result, and we could verify the effectiveness of our proposed method compared to the already existing parallel PBVR method.

关键词： Particle-based Volume Rendering Unstructured volume dataset 234 image composition In-situ visualization

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Group Importance Sampling for particle filtering and MCMC

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DIGITAL SIGNAL processing 2018年 82卷 133-151页

作者： Martino, Luca Elvira, Victor Camps-Valls, Gustau Univ Valencia Image Proc Lab Valencia Spain Univ Carlos III Madrid Dept Stat Signal Proc Madrid Spain IMT Lille Douai CRISTAL UMR 9189 Villeneuve Dascq France

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a wellknown Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard Metropolis-Hastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, two localization problems in a wireless sensor network (with synthetic and real data) and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Importance Sampling Markov Chain Monte Carlo (MCMC) Particle filtering Particle Metropolis-Hastings Multiple Try Metropolis Bayesian inference

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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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A Video Semantic Segmentation Method Based on FCN and Data Argumentation

A Video Semantic Segmentation Method Based on FCN and Data A...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yuan Huang Qian Huang Qinglong Chen Yanping Li Xiaoqing Sun School of Computer and Information Hohai University Nanjing China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Video semantic segmentation is an important and fundamental problem in computer vision. It has broad application prospects in the fields of mobile robot, drone, intelligent driving and monitoring. With the development of neural networks, the models commonly adopted are all based on full convolutional network (FCN). However, current methods are limited by a small training set, which makes it difficult to improve the segmentation accuracy. In this paper, we propose a robust method that uses different data argumentation methods to increase the data set according to different characteristics of the scene. On the basis of analyzing different video features, targeted data argumentation techniques are selected to increase training samples. Experimental results show that data argumentation techniques can significantly improve the accuracy of video semantic segmentation compared with traditional training methods that ignore video features.

关键词： image segmentation Semantics Training Convolution Deep learning image color analysis Neural networks

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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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Robust Feature Matching via Two Constraints Interaction

Robust Feature Matching via Two Constraints Interaction

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yizhang Liu Yanping Li Zheling Xu Xiaoliang Lu Xin Liu Luanyuan Dai Xiong Pan Changcai Yang College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou China College of Computer and Information Hohai University Nanjing China International Business Department Fuzhou Melbourne Polytechnic Fuzhou China MingByte Technology (Qingdao) Co. Ltd

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Feature matching is a critical step in a variety of vision-based applications. However, it is difficult to customize a general method due to complicated image transformations. In this paper, we formulate feature matching into a mathematical problem, which is simple and does not rely on any specific model. Specifically, we construct an objective function based on two constraints (i.e., geometric constraint and consistency constraint). Different from the previous work, we exploit the interaction of these two constraints to reject outliers. Besides, we adopt an iterative strategy to boost the number of inliers. To accelerate the matching process, we use the parallel computing to obtain the information of K nearest neighbors for each feature point. Experimental results on ten image pairs (undergo different image transformations) compared with other state-of-the-arts demonstrate our superiority both in accuracy and efficiency.

关键词： Linear programming Information science Agriculture Forestry Feature extraction Iterative methods Feature detection

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Sequential Detection of Flying Small Target in Infrared images Based on Generalized Low-Rank Background Estimation

Sequential Detection of Flying Small Target in Infrared Imag...

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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： Chuanyun Wang Keyi Si Tian Wang Zhaokui Li Na Lin Ershen Wang Shenyang Aerospace University Shenyang China Beihang University Beijing China

ISBN: (纸本)9781728152103

Addressing the problems of flying small target detection in infrared image sequences, a new approach is proposed based on generalized low-rank background estimation. Firstly, the generalized low rank approximations is introduced to model infrared background image over sequential infrared images. Subsequently, the foreground target image is achieved by background subtraction with the generalized low-rank background estimation. Finally, flying small target detection is achieved over separated target image by threshold segmentation. The experiment results on two infrared image sequences of flying plane demonstrate that the proposed method have effective detection performance and outperform the baseline methods in precision and recall evaluation.

关键词： approximation theory estimation theory image segmentation image sequences infrared imaging object detection Infrared imaging Object detection estimation theory Approximation Theory image sequences sequential detection image segmentation Flying target image baseline method Infrared radiation

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Blind Video Quality Assessment Based on Human Visual Characteristic

Blind Video Quality Assessment Based on Human Visual Charact...

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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： Shiyu Zhou Xiqing Liu Yuanbing Tian Bin Fu Xiuyan Xia Jin Ge China Unicom Network Technology Research Institute Beijing China Zhiyou Open Source Communication Research Institute (Beijing) Co. Ltd Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728152103

Based human visual speed perception characteristic, a video quanlity assessment(VQA) algorithm is introduced in the paper. Natural video statistics features extraction and weighting factors are incorporated in the scheme. In the VQA, considering the impact both video content itself and HVS's characteristic on human subjective perception, we propose weighting factors to scale the effect of those features, and it contains two parts: motion information and perception noise. And natural video statistics features relate to the spatial and temporal domain are extracted. The weighting factors would be used to combine both the temporal and spatial features, then generate the quality of each frame. Finally, the video quality score can be obtained by pooling scheme. LIVE database, EPFL-PoliMI database and some other generated test videos were used in our experiments, and the results indicate our model has outstanding performance.

关键词： feature extraction image denoising image motion analysis statistical analysis video signal processing visual databases image motion analysis visual databases Video signal processing image denoising Feature extraction Videotapes Quality assessment Statistical methods Statistical analysis WEIGHTING FACTORS

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