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International Test Conference in Asia (ITC-Asia)

作者： Liyang Lai Qiting Zhang Hans Tsai Wu-Tung Cheng Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Key Laboratory of Intelligent Manufacturing Technology Shantou University Shantou Guangdong China Mentor a Siemens Business Wilsonville OR US

ISBN: (数字)9781728189444

ISBN: (纸本)9781728189451

GPGPU, general-purpose computing on graphics processing units, has been witnessed growing from a niche to a mainstream computing paradigm in the last decade. It is widely deployed in machine learning, artificial intelligence, and many scientific applications. In this article, we study gate-level logic simulation for scan test patterns, one of the key algorithmic components for test generation, fault grading and design rule check. We are exploring if GPGPU can deliver scalable performance speedup as promised by its massive parallelism. We discuss the limitations of the state of art work. With GPUs' architectural features in mind, a novel algorithm of hybrid race-tolerant parallel logic simulation is proposed to unleash its immense power of parallelization. Its unique integration of oblivious simulation and event-driven simulation leads to a scalable realization of parallel logic simulation for scan patterns. For the first time in the literature, it is demonstrated that a modest GPU can handle an industrial design of over twenty million gates with excellent performance scalability.

关键词： Logic gates Integrated circuit modeling computational modeling Graphics processing units Data models Instruction sets Circuit faults

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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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Design and Research of Face Recognition Simulation System Based on image Preprocessing 11

Design and Research of Face Recognition Simulation System Ba...

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11th International symposium on computational intelligence and Design (ISCID)

作者： Liu, Meng Wang, Dan Huang, Qianqian Shenyang Fire Res Inst Shenyang Liaoning Peoples R China Shenyang Univ Coll Informat Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538685273

In this paper, a series of research on face recognition is carried out by using the function of image processing in MATLAB, and a general face recognition simulation system with a combination of preprocessing and face recognition is implemented. First, the image is preprocessed, and then the graphical user interface is established. In order to achieve the goal of face recognition, the PCA method is used to compare the database. The simulation results show that the face recognition simulation system can realize the recognition of the face image accurately.

关键词： Preprocessing PCA MATLAB Face Recognition

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ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODELING TO INTERPRETABILITY

ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODE...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gustau Camps-Valls Markus Reichstein Xiaoxiang Zhu Devis Tuia Image Processing Laboratory (IPL) Universitat de València Max Planck Institute for Biogeochemistry Germany Technical University Munich (TUM) & German Aerospace Center (DLR) Munich Germany Wageningen University and Research (WUR) The Netherlands

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and they cannot cope with non-Euclidean spaces efficiently. Second, as other data-driven techniques, deep learning models do not necessarily respect physical or causal relations. Finally, deep learning models are still obscure and resistant to interpretability. Advances are needed to cope with arbitrary signal structures and data relations, physical plausibility and interpretability. This paper discusses about ways forward to develop new DL methods for the Earth sciences in all three directions.

关键词： Earth Deep learning Remote sensing computational modeling Machine learning Predictive models Data models

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Near-Lossless Deep Feature Compression for Collaborative intelligence 20

Near-Lossless Deep Feature Compression for Collaborative Int...

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20th ieee International Workshop on Multimedia signal processing (MMSP)

作者： Choi, Hyomin Bajic, Ivan, V Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9781538660706

Collaborative intelligence is a new paradigm for efficient deployment of deep neural networks across the mobile cloud infrastructure. By dividing the network between the mobile and the cloud, it is possible to distribute the computational workload such that the overall energy and/or latency of the system is minimized However, this necessitates sending deep feature data from the mobile to the cloud in order to perform inference. In this work, we examine the differences between the deep feature data and natural image data, and propose a simple and effective near-lossless deep feature compressor. The proposed method achieves up to 5% bit rate reduction compared to HEVC-Intra and even more against other popular image codecs. Finally, we suggest an approach for reconstructing the input image from compressed deep features that could serve to supplement the inference performed by the deep model.

关键词： Deep feature compression collaborative intelligence deep neural network input reconstruction

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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ieee transactions on image processing : a publication of the ieee signal processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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image processing Strategies Based on Deep Neural Network for Simulated Prosthetic Vision 11

Image Processing Strategies Based on Deep Neural Network for...

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11th International symposium on computational intelligence and Design (ISCID)

作者： Zhao, Ying Li, Qi Wang, Donghui Yu, Aiping Inner Mongolia Univ Sci & Technol Sch Informat Engn Baotou Inner Mongolia Peoples R China

ISBN: (纸本)9781538685273

Due to the limited number of implantable electrodes, correcting the input image such that the electrode stimulus ultimately reaching the visual pathway contains sufficient topological information is a challenging task. Some image processing strategies have been applied to the image-to electrode mapping process previously in order to obtain better recognition performance under simulated prosthetic vision. In this work, a method for foreground extraction and pixelation of images containing simple objects using the state-of-the-art deep learning techniques was proposed. For that, accurate foreground extraction results were obtained by training the U-net network model, pixelated them and paired with the original images. These paired samples were then used to train a Pix2pix generative adversarial network in order to achieve the image-to-pixelated image translation. The experimental results indicated that the U-net network had better foreground extraction effect than the traditional image processing strategies, and the pixelated images generated by the Pix2pix generative model contained more abundant and precise details than other strategies.

关键词： simulated prosthetic vision image processing strategies deep neural networks

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A registration and change detection algorithm based on pre- and post-strike image 11

A registration and change detection algorithm based on pre- ...

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11th International symposium on computational intelligence and Design (ISCID)

作者： Liu, Zihe Wang, Yuhui Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China

ISBN: (纸本)9781538685273

image-based damage assessment requires processing and analysis of two images taken at the same location and at different times. For the registration of this type of image, the traditional method based on gray information is not applicable due to the change of the image itself. image registration based on Fourier-Mellin Transform is a global and phase correlation method which is carried on Cartesian coordinate and Log-polar coordinate. This method extracts the extreme value of the impulse function of the image phase in the frequency domain, and obtains the transformation parameters of the image. In combination with the SIFT registration method, the effect of image registration with a large rotation angle is improved. Experiments show that the algorithm is effective and feasible. Based on the registration, the difference method is used to extract the image change area. The adaptive threshold method is used to preliminarily determine the interested targets, which provides a basis for the establishment of the damage model in the next step.

关键词： damage assessment image registration fourier transform change detection

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Joint Optimization Offloading and Resource Allocation in Vehicular Edge Cloud Computing Networks with Delay Constraints

Joint Optimization Offloading and Resource Allocation in Veh...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xueying Liu Guanglin Zhang College of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

The development of 5G technology has led to the rapid construction of the Internet of Vehicles (IOV). The emergences of various IOV applications like autonomous driving require unlimited low-latency computation resources, which can be provided by Mobile edge computing (MEC) technique. In this paper, we study a framework of IOV with a cloud computing center that supports edge computing, aiming at improving the quality of service (QoS) for vehicles. We consider delay-sensitive tasks and non-delay-sensitive tasks, which are classified according to their task processing delay constraints. We formulate the optimization problem as a system utility maximization problem, searching for the optimal QoS. Based on the distribution method and the ant colony algorithm (ACO), we propose an optimization approach with joint offloading selection strategy and computing resource distribution strategy. Lastly, we show the simulation results, which demonstrates the effectiveness and high-efficiency of the proposed algorithm.

关键词： Cloud computing computational modeling Quality of service Delays Resource management Task analysis Optimization

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CNN-based Super-resolution Reconstruction for Traffic Sign Detection

CNN-based Super-resolution Reconstruction for Traffic Sign D...

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ieee symposium Series on computational intelligence (SSCI)

作者： Fan Wang Jianqi Shi Xuan Tang Jielong Guo Peidong Liang Yuanzhi Feng College of Electrical Engineering and Automation Fuzhou university Fujian China Shanghai Key Lab for Trustworthy Computing School of Software Engineering East China Normal University Shanghai Putuo District Quanzhou Institute of Equipment Manufacture Haixi Institutes Chinese Academy of Sciences Quanzhou China Quanzhou HIT Research Institute of Engineering and Technology Quanzhou Fengze District China Henan University North Section of Jinming Avenue Kaifeng Longting District China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Automatic identification for traffic signs is an important part of intelligent driving and traffic safety. Deep learning has already made a great achievement in traffic sign detection. However, the camera on a car may capture a low resolution and blurry image in certain environments, which make it inaccurate for traffic sign detection. Therefore, we propose a method based on image super-resolution reconstruction for improving the detection rate of traffic signs. Firstly, a low-resolution image is transformed by CNN-based super-resolution network into a high-resolution one. Then, to meet the requirements of on-line processing, we use the generated super-resolution image as input for the detection network with 16 filters in this layer. At last, we separately trained two CNNs for super-resolution reconstruction and traffic sign detection, which reduce the processing time. Experimental results demonstrate that our model can achieve better performance than the existing methods for traffic sign detection.

关键词： image resolution Convolution Feature extraction signal resolution image reconstruction Object detection Machine learning

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