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IEEE International Conference on System Science and Engineering

作者： Ying-Hao Wang Hao-En Cheng Chih-Jui Lin Ri-Wei Deng Hsuan Lee Tzuu-Hseng S. Li aiRobots Laboratory Department of Electrical Engineering National Cheng Kung University

ISBN: (纸本)9781479900084

This paper mainly discusses the realization of affine Scale-Invariant Feature Transform (SIFT) real-time image processing method for "May" who is a home service robot designed and implemented by aiRobots laboratory. Firstly, a six-step method is proposed to create image database. Then the SIFT algorithm is adopted to detect features with this database, the error rate of features is reduced. Integration of the six-step method and SIFT can significantly decrease time for setting up the database. Thirdly, the object-recognition vision system is built up by parallel Affine-SIFT (ASIFT) algorithm, where the OpenGL is used to reduce the calculation time of affine projection and SiftGPU (Graphics processing Unit) is adopted to accomplish the real-time operation speed of SIFT. Finally, the real-time grasping experiments demonstrate the feasibility and validity of the proposed scheme.

关键词： Affine-SIFT Home service robot May OpenGL real-time image processing SIFT real-time image processing Sifting Service robots computational time Affine In-law visual databases

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real-time parallel image processing applications on multicore CPUs with OpenMP and GPGPU with CUDA

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JOURNAL OF SUPERCOMPUTING 2018年第6期74卷 2255-2275页

作者： Aydin, Semra Samet, Refik Bay, Omer Faruk Gazi Univ Ankara Turkey Ankara Univ Ankara Turkey

This paper presents real-time image processing applications using multicore and multiprocessing technologies. To this end, parallel image segmentation was performed on many images covering the entire surface of the same metallic and cylindrical moving objects. Experimental results on multicore CPU with OpenMP platform showed that by increasing the chunk size, the execution time decreases approximately four times in comparison with serial computing. The same experiments were implemented on GPGPU using four techniques: (1) Single image transmission with single pixel processing;(2) Single image transmission with multiple pixel processing;(3) Multiple image transmission with single pixel processing;and (4) Multiple image transmission with multiple pixel processing. All techniques were implemented on GeForce, Tesla K20 and Tesla K40. Experimental results of GPU with CUDA platform showed that by increasing the core number speedup is increased. Tesla K40 gave the best results of 35 and 12 (for the first technique), 36 and 13 (for the second technique), 54 and 16 (for the third technique), 71 and 17 (for the fourth technique) times improvement without and with data transmission time in comparison with serial computing. As a result, users are suggested to use Tesla K40 GPU and Multiple image transmission with multiple pixel processing to get the maximum performance.

关键词： Parallel computing real-time image processing image segmentation Thresholding Multicore programming GPU programming

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A Distributed Multiprocessor System Designed for real-time image processing

A Distributed Multiprocessor System Designed for Real-Time I...

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International Conference on Experimental Mechanics 2008 (ICEM 2008)

作者： Zhiyi Yin,Wei Heng National Mobile Communications Research Laboratory,Southeast University Nanjing,China

In real-time image processing,a large amount of data is needed to be processed at a very high *** an ordinary computer with single processor has usually certain difficulties,taking its limited capacity into *** dedica... 详细信息

关键词： real-time image processing distributed algorithm distributed multiprocessor system

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The role of film and television big data in real-time image detection and processing in the Internet of Things era

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JOURNAL OF real-time image processing 2021年第4期18卷 1115-1127页

作者： Tong, Yangfan Sun, Wei Wuhan Univ Sch Art Wuhan 430072 Peoples R China Hunan Univ Technol & Business Sch Literature & Journalism Changsha 410205 Peoples R China

With the rapid development of the Internet, images play an increasingly critical role as an important data source in the Internet of Things (IoT). To meet the low-latency and high-efficiency transmission method of the IoT platform, the current problems in the image processing are analyzed from the perspective of real-time image processing in this study. The image features are extracted with the back propagation neural network (BPNN), and the images are classified with the support vector machines (SVM). A real-time image detection and processing platform (RT-IDPP) is constructed using the Adaboost framework based on the IoT, and the real-time image transmission and processing is realized based on different databases. It is found that the RT-IDPP proposed for the IoT realizes the image detection and tracking. The proposed method can not only run effectively on different cloud platforms for use, but also meet the real-time requirements in the image detection and tracking process, ensuring that the image detection rate is higher than 97%. Thus, the detection effect is better. Compared with the traditional image detection methods, the proposed method has higher detection rate and lower false-negative rate (FNR) and false-positive rate (FPR). The experimental detection effect on the film and television big data (FTBD) database is significantly better than that of other databases. This research can provide a theoretical basis for related researches on real-time image processing in the environment of IoT.

关键词： Internet of Things Film and television big data real-time image processing Deep learning Adaboost framework

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NeNEB - An application adjustable single chip neural network processor for mobile real time image processing

NeNEB - An application adjustable single chip neural network...

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International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/image processing

作者： Larsson, L Krol, S Lagemann, K UNIV HAMBURG DEPT COMP SCIGRP TECHD-22527 HAMBURGGERMANY

ISBN: (纸本)0818674563

A digital implementation of the recall phase of a backpropagation neural network for real time image classification is presented. The motivation has been, that parallelism of a neural network has less advantage, if the input data stream is sequential such as the pixel stream of an usual CCD camera. In addition, classifying a stream of pixels with a single chip neural processor implanted into a camera avoids the bottleneck caused by image data transfer and storage, which is neglected often. But the chip will not be restricted to real time image processing applications. The chip realizes a network with 32 output neurons, 8 hidden neurons and up to 64 K inputs (/spl ap/512 K synapses). It is estimated to classify 50 gray scale images per second of size up to 256/spl times/256 pixels, which complies to 26 M cps. The weights of hidden neurons are stored in external memory, whereas weights of output neurons are stored on-chip.

关键词： Neural network pixel Network Processor real-time image processing external memory PROCESSOR Charge Coupled Device camera image processing streams parallelism Neurons

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B3: A plug-n-play Internet enabled platform for real time image processing 14

B3: A plug-n-play Internet enabled platform for real time im...

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14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)

作者： Aggarwal, Ankit Gandhi, Gaurav MLabs Res New Delhi India

ISBN: (纸本)9781479960071

A plug-n-play internet enabled platform for real time simulation of image processing algorithms (like CNN) is demonstrated on the Bit by Bit platform. The prototyping platform uses Raspberry Pi, thereby enabling collaboration and sharing of the image processing simulation experiments and results over the internet. These algorithms can be applied to the real time captured images and results are displayed instantly over the internet or via email.

关键词： Internets real-time image processing Binary digits Platform real-time simulation image processing algorithm image processing electronic mail

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A flexible software architecture for scalable real-time image and video processing applications

A flexible software architecture for scalable real-time imag...

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Conference on real-time image and Video processing

作者： Usamentiaga, Ruben Molleda, Julio Garcia, Daniel F. Bulnes, Francisco G. Univ Oviedo Dept Comp Sci & Engn Gijon 33204 Asturias Spain

ISBN: (纸本)9780819491299

real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility because they are normally oriented towards particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse and inefficient execution on multicore processors. This paper presents a novel software architecture for real-time image and video processing applications which addresses these issues. The architecture is divided into three layers: the platform abstraction layer, the messaging layer, and the application layer. The platform abstraction layer provides a high level application programming interface for the rest of the architecture. The messaging layer provides a message passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of messages. The application layer provides a repository for reusable application modules designed for real-time image and video processing applications. These modules, which include acquisition, visualization, communication, user interface and data processing modules, take advantage of the power of other well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, we present different prototypes and applications to show the possibilities of the proposed architecture.

关键词： Software architecture parallel image processing real-time image processing

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C64x-based multi-DSP real-time image processing system

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Jisuanji Gongcheng/Computer Engineering 2006年第11期32卷 268-270页

作者： Hu, Junhong Jiang, Haoyang Fan, Rong Zhang, Tianxu Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

A C64x-based multi-DSP real-time image processing system is introduced, which uses high performance TMS320C6414 DSP to process image and FPGA device to realize LINK port to transport image data with LVDS signal. Requirements of image processing performance and image data communication of image fusion are met. Based on the hardware system, a real time microkernel based distributed operating system is designed and implemented. At the end, its real-time performance is analyzed from three aspects. It's shown that the real time image processing system can reach the requirements of real time image processing.

关键词： FPGA Multi-DSP system real-time image processing

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Flexible DSP-based network for real-time image-processing

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Wuhan University Journal of Natural Sciences 2004年第6期9卷 921-926页

作者： Mao, Hai-Cen Zhang, Tian-Xu Jiang, Hao-Yang Wang, Yue-Huan Inst. for Pattern Recognition Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

This paper proposed a general purpose real-time image processing system based on a flexible DSP-based Network, which is implemented by a high bandwidth communication channel, links. The links is realized using FPGA and provides a bandwidth of 12.8 Gbit/s. Using the links, the topologic of multi-DSP system can be changed online to meet the variabilities of the parallel algorithm of image processing. The system can be assembled with utmost tens of boards and maintain the high communication speed. Analysis of the system adaptivity to image processing is testified followed by actual results.

关键词： Flexible FPGA Links Multi-DSP real-time image processing Scalable

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Fast FPGA Prototyping based real-time image and Video processing with High-Level Synthesis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第2期11卷 108-116页

作者： Ghodhbani, Refka Horrigue, Layla Saidani, Taoufik Atri, Mohamed Univ Monastir Lab Elect & Microelect Fac Sci Monastir Monastir Tunisia Northern Border Univ Rafha Dept Comp Sci Fac Comp & Informat Technol Rafha Saudi Arabia King Khalid Univ Coll Comp Sci Abha Saudi Arabia

Programming in high abstraction level is known by its benefits. It can facilitate the development of digital image and video processing systems. Recently, high-level synthesis (HLS) has played a significant role in developing this field of study. real time image and video processing solution needing high throughput rate are often performed in a dedicated hardware such as FPGA. Previous studies relied on traditional design processes called VHDL and Verilog and to synthesize and validate the hardware. These processes are technically complex and time consuming. This paper introduces an alternative novel approach. It uses a Model-Based Design workflow based on HDL Coder (MBD), Vision HDL Toolbox, Simulink and MATLAB for the purpose of accelerating the design of image and video solution. The main purpose of the present paper is to study the complexity of the design development and minimize development time (time to market: TM) of conventional FPGA design. In this paper, the complexity of the development (TM) can be reduced by 60% effectively by automatically generating the IP cores and downloading the modeled design through the Xilinx tools and give more advantages of FPGA related to the other devices like ASIC and GPU.

关键词： High-level synthesis FPGA fast prototyping real-time image processing video surveillance computer-aided design model-based design HDL coder FPGA

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