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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Research on Real-Time Reasoning Based on JetSon TX2 Heterogeneous Acceleration YOLOv4

Research on Real-Time Reasoning Based on JetSon TX2 Heteroge...

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IEEE International conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Zhang Qiang Wang Yuanyu Zhu Liang Zhang Jin Lin Yu Lin Dandan Kunming Institute of Physics Kunming China

ISBN: (纸本)9781665423113;9781665446327

Aiming at the problem of high CPU resource occupancy rate and large delay on the embedded side when the YOLOv4 target detection algorithm is in model inference. This research proposes an embedded platform based on JetSon TX2, using CPU+GPU heterogeneous mode, deploying a YOLOv4 network model improved by the K-means clustering algorithm, and real-time detection of infrared long-wave images containing 10 types of military targets. Using the advantages of CPU multicore serial, load the weight file of the YOLOv4 model and the cache of the input matrix; GPU core CUDA core accelerates the convolution and clustering of the YOLOv4 model. The experimental results show that two independent experiments of CPU, CPU+GPU on Jetson TX2 platform, the Mean Average Precision of YOLOv4 model inference is 93%; the delay of detecting a single long-wave infrared image is 21267.9ms and 402.4ms respectively; When the target in the video stream, the frame rate of the CPU is less than 0.1FPS, and the frame rate of the GPU can reach 3.2FPS; the average occupancy rate of multi-core CPU resources is 96.3% and 45.0% respectively. It can be seen that when the mAP of the detection target is 93%, the resource consumption and delay of the CPU+GPU heterogeneous mode are better than the CPU architecture, which can provide a reference for the subsequent real-time detection of military targets in the actual combat environment.

关键词： Training Multicore processing Graphics processing units Clustering algorithms Streaming media Real-time systems Inference algorithms

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A Power-aware Scheduling Algorithm in Multi-tenant IaaS Clouds 4

A Power-aware Scheduling Algorithm in Multi-tenant IaaS Clou...

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4th IEEE International conference on Signal and image processing (ICSIP)

作者： Liang, Bin Dong, xiaoshe Zhang, xingjun Xi An Jiao Tong Univ Xian 710049 Peoples R China Xian Shiyou Univ Xian 710065 Peoples R China

ISBN: (纸本)9781728136608

With the rise and development of cloud computing technology, more and more individuals and companies are hosting applications to cloud servers. Thanks to the strong support of virtualization technology, the development of cloud computing technology has become faster. Virtualization technology can isolate the user from the underlying physical hardware and generate virtual machines of various configurations for the user. However, as a basic unit for cloud users, virtual machine scheduling algorithms have become a research hotspot in academia and industry. In the existing research, most of the virtual machine scheduling algorithms are aimed at simplifying or satisfying user requirements. However, with the further expansion of the cloud cluster size and the increase of multi-tenant parallel tasks, the existing virtual machine scheduling algorithm can not meet the current requirements. In this paper, according to the characteristics of virtual machine and physical machine, a new virtual machine scheduling algorithm is proposed: Maximum Filling (MF) Algorithm, which can improve the scheduling efficiency of virtual machines and reduce the usage of physical machines in cloud clusters. Finally, the effect of the algorithm is compared. The proposed algorithm is obviously better than the comparison algorithm.

关键词： virtualization cloud cluster low cost scheduling algorithm virtual machine physical machine

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Why is the Winner the Best?

Why is the Winner the Best?

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. xia J. xie Z. xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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A Human Skeleton Data Optimization Algorithm For Multi-Kinect

A Human Skeleton Data Optimization Algorithm For Multi-Kinec...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Hanzhen Zhang xiaojuan He Yuehu Liu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Software Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

As a motion capture device, Kinect can extract human skeleton data (three dimensional positions of body joints) through depth information, which has been increasingly applied in many fields such as action recognition, medical diagnosis and rehabilitation evaluation. But when collecting human skeleton data with a single Kinect, the detection range is limited and stale data exceptions or data missing might occur due to the influence of human body occlusion and environment. These invalid data will directly affect the accuracy of relevant calculation results and hinder the application of Kinect. To solve the above problems, we propose a data acquisition scheme based on multi-Kinect and an optimization algorithm for human skeleton data. First, we use the method based spatial region constraints and K-means clustering to remove the outliers in the human skeleton data collected by each Kinect. Then we convert the data from different perspectives into the same coordinate system and perform adaptive weighted fusion. We finally obtain a stable and valid data sequence, facilitating its application greatly. We design relevant experiments to compare the optimized data with the original data and the results verify the effectiveness of the algorithm.

关键词： multi-kinect skeleton data outlier detection data fusion Data fusion game player data acquisition programs data optimization Joint Loose Bodies Coordinate system data integration Optimization algorithms RAW DATA algorithms outliers

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ISAR Imaging Based on Block Bayesian Compressed Sensing by Learning the Clustering Structure

ISAR Imaging Based on Block Bayesian Compressed Sensing by L...

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International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Iman Faramarzi Rahim Entezari Alijabbar Rashidi Faculty of Electrical and Computer Engineering Malek-e-Ashtar University of Technology Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

In conventional inverse synthetic aperture radar (ISAR) imaging methods based on compressed sensing (CS), some of the weak scatter points cannot be recovered completely with a short coherent processing interval (CPI). Therefore in this paper, according to the continuity pattern of the ISAR image and location of the dominant scatter points in some clusters, we present a high-resolution ISAR imaging method based on Block Bayesian Compressed Sensing to reconstruction of unknown Sparse Clustering Structure, which is named as BBCS-SCS. For this reason, we first use complex Gaussian prior distribution to model statistical dependencies between neighboring dominant scatterers. Then, a variational Bayesian (VB) inference has been designed and developed to learn the clustering structure of scatterers in the target scene. Simulation results show that the proposed algorithm has better performance in terms of image contrast, image entropy, and running time in comparison with other methods in strong noise.

关键词： Imaging Signal processing algorithms Clustering algorithms Inference algorithms Bayes methods Inverse synthetic aperture radar Compressed sensing

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FPGA Implementation of Low Power image Compression Technique

FPGA Implementation of Low Power Image Compression Technique

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International conference on Advanced Computing and Communication systems (ICACCS)

作者： R Dhanagopal C Rajeswari R Sureshkumar R Menaka Chennai Institute of Technology Chennai Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

Booth multiplier has inherent advantage over normal binary multiplier. it's been adopted by Booth algorithm. This algorithm is replaced the conventional multiplier in the image compression algorithms like Discrete cosine transform (DCT) to reduce the power dissipation. The goal is to implement a Booth multiplier, Integrate with DCT and to prove booth multiplier is best so to offer comparison of binary multiplier and booth multiplier. Reducing the power dissipation area is the main goal in the design of portable computing, image processing, video processing and communication devices. Therefore different types of digital signal-processing chips are design with low power. In digital signal processing multiplier will play the major role to perform any applications. This proposed method will provide less power dissipation and less area.

关键词： Discrete cosine transforms Computer architecture Signal processing algorithms Computational modeling Mathematical model Matlab Performance evaluation

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Deep Optics: Learning Cameras and Optical Computing systems

Deep Optics: Learning Cameras and Optical Computing Systems

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Asilomar conference on Signals, systems & Computers

作者： Gordon Wetzstein Hayato Ikoma Christopher Metzler Yifan Peng Stanford University

Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we review recent hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can facilitate application-domain-specific cameras or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems.

关键词： Computer vision image processing Neural networks Memory management Optical computing Optical fiber networks Optics

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A System for Semi-automatic Construction of image processing Pipeline for Complex Problems 1

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20th International conference on Business Process Modeling, Development, and Support (BPMDS) / 25th International conference on Exploring Modeling Methods for systems Analysis and Design EMMSAD)

作者： Rajbhoj, Asha Deshpande, Shailesh Gubbi, Jayavardhana Kulkarni, Vinay Balamuralidhar, P. TCS Res Pune Maharashtra India TCS Res Bangalore Karnataka India

ISBN: (数字)9783030206185

ISBN: (纸本)9783030206185;9783030206178

Creation of an image processing pipeline for solving complex problems is a tedious task. Current industry practices largely rely on the image processing domain experts for this. Given the image processing problem have multiple viable solutions. Thus, the search space of creating suitable solution using available algorithms for a given goal in a given constrained infrastructure is generally large. The exploratory work to choose an optimal image processing solution is an effort-, time- and intellect-intensive endeavor. To address these issues we propose a system for automatic construction of the pipeline that can improve domain expert's productivity by creating a solution quickly. The proposed system externalizes image processing domain knowledge in the form of object model and a set of rules defined over it. Recommendations are given to choose suitable algorithm/s for carrying out the image processing tasks. On successful creation of the pipeline, the system generates deployable code. It also generates trace data that can help for cognitive knowledge upgrade. We showcase ongoing work on this system and its early results using the simple working example.

关键词： image processing Recommender system Meta model Model based system Model engineering

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A Hardware-Efficient Post-processing Algorithm for Motion Compensated Frame Rate Up-Conversion

A Hardware-Efficient Post-Processing Algorithm for Motion Co...

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IEEE International Symposium on Circuits and systems (IEEE ISCAS)

作者： Wang, Hang Wang, Tiancheng Mi, Yunqi Sun, Hongbin Zheng, Nanning Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781728103976

Post-processing is an important module in motion compensated frame rate up-conversion design, and has a direct impact on the image quality of interpolated frame. However, how to balance between image quality and computational efficiency is still very challenging for post-processing, especially in hardware design. This paper proposes a hardware-efficient post-processing algorithm which leverages the temporal and spatial constraints to locally refine interpolated pixels. Moreover, we employ the quantization and approximation techniques to further reduce the computational intensity of the proposed post-processing algorithm. The quality of interpolated frame has been greatly improved both in objective and subjective aspects. The proposed post-processing algorithm is extensively evaluated by a set of video test sequences. Evaluation results demonstrate that, compared with the reference designs, the proposed algorithm can improve PSNR by at least 1.69 dB with comparable computational complexity.

关键词： Hardware Optimization Real-time systems image quality Quantization (signal) Interpolation Approximation algorithms

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Fingerprint Feature Recognition of Frequency Hopping Radio with FCBF-NMI Feature Selection 8th

Fingerprint Feature Recognition of Frequency Hopping Radio w...

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8th International conference on Communications, Signal processing, and systems, CSPS 2019

作者： Li, Hongguang Guo, Ying Qi, Zisen Sui, Ping Su, Linghua Institute of Information and Navigation Air Force Engineering University Xi’an Shaanxi710077 China

ISBN: (纸本)9789811394089

High-dimensional features have great advantages for the analysis and identification of radio fingerprint features. In order to enhance the classification and recognition capability of frequency hopping stations, it is usually necessary to increase the feature type and dimension to further improve the classification accuracy of the classifier. However, with the increase of the feature types and the dimensions, a large number of irrelevant and redundant features will be introduced, which leads to the increased classification time and the low classification accuracy. In order to reduce the feature dimension and remove redundant features, a FCBF feature selection algorithm based on normalized mutual information was proposed, named FCBF-NMI. The algorithm uses normalized mutual information instead of symmetric uncertainty as the correlation evaluation standard of FCBF algorithm, and analyzes the correlation between features and categories, deletes irrelevant and redundant features, and finally obtains optimal feature subset. Experimental results show that, FCBF-NMI can obtain the reasonable optimal features, on the base of guaranteeing the correct classification rate, the computing time can be reduced, and the effectiveness of feature recognition and the generalization ability of classification algorithms can be improved as well. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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