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A center-driven image set partition algorithm for efficient structure from motion

INFORMATION SCIENCES 2019年 479卷 101-115页

作者： Sun, Kun Tao, Wenbing China Univ Geosci Sch Comp Sci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China

This paper proposes a novel center-driven image set partitioning method dedicated for efficient Structure from Motion (SfM) on unevenly distributed images. First, multiple base clusters are found at places with high image density. Instead of building a small initial model from two images, we build multiple initial base models from these base clusters. This promises that the scene is reconstructed from dense places to sparse areas, which can reduce error accumulation when images have weak overlap. Second, the whole image set is divided into several region clusters to decide which images should be reconstructed from the same base model. In this step, the base models are treated as centers and the affinity between an image with each of them is measured by the reconstruction path length. To enable faster speed, images in each region cluster are further divided into several sub-region clusters so that they could be added to the same base model simultaneously. Based on the above partitioning results, the partial 3D models are reconstructed in parallel and then merged. Experiments show that the proposed method achieves remarkable speedup and better completeness than state-of-the-art methods, without significant accuracy deterioration. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Center-driven image set partitioning 3D reconstruction Structure from Motion

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The Research of Implementation Method of Canny Edge Detection of Video on FPGA

The Research of Implementation Method of Canny Edge Detectio...

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Information Science, parallel and distributed Systems (ISPDS), International Conference on

作者： Li Quan Tan Bin He School of information science and technology Beijing University of chemical technology

ISBN: (数字)9781728196688

ISBN: (纸本)9781728196695

Edge detection algorithm has an important application in video image processing. The algorithm overcomes the shortcomings of traditional video image edge detection methods, and significantly improves the quality of video image edge detection. However, the amount of calculation is very large, and the traditional implementation method is difficult to meet the real-time requirements of video image edge detection, and the implementation cost is high. In this design, field programmable gate array (FPGA) is used as the core to construct and implement Canny video image edge detection system. Firstly, the video image is collected by ov7725 camera in real time; then, the edge detection of video image based on Canny algorithm is implemented in FPGA. Finally, the effect of Canny video image edge detection is compared and analyzed. This design has a good reference value for canny video image edge detection.

关键词： image edge detection Information science image filtering

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Joint Channel Pruning and Quantization-Based CNN Network Learning with Mobile Computing-Based image Recognition

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2021年第1期2021卷

作者： Liu, Huanyu Luo, Qing Shao, Mingmei Pan, Jeng-Shyang Li, Junbao Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Peoples R China Shenyang Aircraft Design & Res Inst Shenyang 110035 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

The development of the Internet and communication technology has ushered in a new era of the Internet of Things (IoT). Moreover, with the rapid development of artificial intelligence, objects are endowed with intelligence, such as home automation and smart healthcare, which are typical applications of artificial intelligence technology in IoT. With the rise of convolutional neural network (CNN) in the field of computer vision, more and more practical applications need to deploy CNN on mobile devices. However, due to the large amount of CNN computing operations and the large number of parameters, it is difficult to deploy on ordinary edge devices. The neural network model compression method has become a popular technology to reduce the computational cost and has attracted more and more attention. We specifically design a small target detection network for hardware platforms with limited computing resources, use pruning and quantization methods to compress, and demonstrate in VOC dataset and RSOD dataset on the actual hardware platform. Experiments show that the proposed method can maintain a fairly accurate rate while greatly speeding up the inference speed.

关键词： Internet of things

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Norm-Aware Embedding for Efficient Person Search

Norm-Aware Embedding for Efficient Person Search

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Di Zhang, Shanshan Yang, Jian Schiele, Bernt Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ PCA LabKey Lab Intelligent Percept & Syst High D Nanjing Peoples R China Jiangsu Key Lab Image & Video Understanding Socia Nanjing Peoples R China Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781728171685

Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID). Specifically, it requires to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. One major challenge comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two sub-tasks in a joint person search model. To this end, we present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by misalignment. We outperform other one-step methods by a large margin and achieve comparable performance to two-step methods on both CUHK-SYSU and PRW. Also, our method is easy to train and resource-friendly, running at 12 fps on a single GPU.

关键词： Embeddings

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Design and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with parallel Association Rule Mining and Scalable Recommenders

Design and Evaluation of an HPC-based Expert System to speed...

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Proceedings of the International Convention MIPRO

作者： C. Barakat M. Riedel S. Brynjólfsson G. Cavallaro J. Busch R. Sedona School of Engineering and Natural Sciences University of Iceland Iceland Jülich Supercomputing Centre Forschungszentrum Jülich Germany

Given the Covid-19 pandemic, the retail industry shifts many business models to enable more online purchases that produce large transaction data quantities (i.e., big data). Data science methods infer seasonal trends about products from this data and spikes in purchases, the effectiveness of advertising campaigns, or brand loyalty but require extensive processing power leveraging High-Performance Computing to deal with large transaction datasets. This paper proposes an High-Performance Computing-based expert system architectural design tailored for ‘big data analysis’ in the retail industry, providing data science methods and tools to speed up the data analysis with conceptual interoperability to commercial cloud-based services. Our expert system leverages an innovative Modular Supercomputer Architecture to enable the fast analysis by using parallel and distributed algorithms such as association rule mining (i.e., FP-Growth) and recommender methods (i.e., collaborative filtering). It enables the seamless use of accelerators of supercomputers or cloud-based systems to perform automated product tagging (i.e., residual deep learning networks for product image analysis) to obtain colour, shapes automatically, and other product features. We validate our expert system and its enhanced knowledge representation with commercial datasets obtained from our ON4OFF research project in a retail case study in the beauty sector.

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基于并行计算的抛光轴表面缺陷检测研究

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机电工程 2020年第12期37卷 1540-1546页

作者：姜庆胜李研彪计时鸣浙江工业大学机械工程学院浙江杭州310023

针对在生产实践中抛光轴表面缺陷检测的实时性和缺陷分类需求问题,对抛光轴表面缺陷检测系统、缺陷图像采集、图像特征、传统检测方法等方面进行了研究,归纳了轴表面缺陷检测的处理方法。提出了两种基于并行计算的检测系统,即基于云的Sp... 详细信息

针对在生产实践中抛光轴表面缺陷检测的实时性和缺陷分类需求问题,对抛光轴表面缺陷检测系统、缺陷图像采集、图像特征、传统检测方法等方面进行了研究,归纳了轴表面缺陷检测的处理方法。提出了两种基于并行计算的检测系统,即基于云的Spark多机并行的分布式计算机集群检测系统和单机多核的CUDA并行处理检测系统;通过Spark架构多机分布式并行计算机理的分析,以及结合CUDA单机多核并行运算原理,从数学角度进行了并行化处理,最后进行了相应的实验。研究结果表明:基于CUDA的单机多核的并行处理检测系统适合实时检测,基于Spark的多机分布式并行检测系统不适合实时检测系统,但适合大数据处理系统。

关键词：图像处理 Spark 分布式并行计算图像处理单元 CUDA

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Supervised Multi-View distributed Hashing

Supervised Multi-View Distributed Hashing

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IEEE International Conference on image processing

作者： Yunpeng Tang Xiaobo Shen Zexuan Ji Tao Wang Peng Fu Quan-Sen Sun School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized sub-problems. The sub-problems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

关键词： distributed databases Convex functions Benchmark testing Linear programming Computational modeling Binary codes Optimization

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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...

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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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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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Color image watermark decoder by modeling quaternion polar harmonic transform with BKF distribution

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SIGNAL processing-image COMMUNICATION 2020年 88卷 115946-115946页

作者： Liu, Yunan Zhang, Shanshan Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn PCA LabMinist Educ Key Lab Intelligent Percept & Syst High Dimens In Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210094 Peoples R China

It still remains a challenging problem to improve both of the imperceptibility and robustness for the digital image watermarking task. In this study, a color image watermarking scheme is proposed based on quaternion polar harmonic transform (QPHT) with maximum likelihood decoder, which better balances imperceptibility and robustness in a novel fashion. Thanks to the strong robustness of QPHT, the digital watermark is embedded into the QPHT magnitudes using a multiplicative approach. M the receiver, the watermark decoder can be formulated as a problem of weak signal detection, as the QPHT magnitudes are highly non-Gaussian. In view of this, selection of proper models is of great importance to construct a suitable watermark decoder. Further, we validate that the probability density function of Bessel K form suitably models the heavy tail behavior of QPHT magnitudes, and thus the maximum likelihood decoder is developed by modeling QPHT magnitudes with Bessel K form distribution. Experimental results on standard benchmarks demonstrate that the proposed approach provides better imperceptibility and robustness against various kinds of attacks compared to previous state-of-the-art methods.

关键词： Color image watermarking Quaternion polar harmonic transforms Bessel K form distribution Maximum likelihood decoder

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