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

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Dynamic Load Balancing of Multiple Controller based on Intelligent Collaboration in SDN

Dynamic Load Balancing of Multiple Controller based on Intel...

引用

Computer Vision, image and Deep Learning (CVIDL), International conference on

作者： Shike Yang Haobin Shi Hengsheng Zhang The Twentieth Research Institute of China Electronic Technology Group Corporation Xi’an China Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194820

In order to solve the problem of switch migration conflict caused by system performance difference and frequent communication in SDN multiple controller load balancing process, a multiple domain controller cluster is established by considering the real-time load and communication consumption of the controller. In each domain, the switch in the overload controller is moved to the lighter controller by adopting a request response model combined with an improved SARSA reinforcement learning algorithm for balancing the load of the system. In this way, the load balance among multiple controllers is achieved, and the load capacity of the system is improved. The simulation experiment shows the feasibility of the proposed method.

关键词： Control systems Switches Load management Process control Servers Load modeling Heuristic algorithms

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Visual Enhancement Techniques For Underwater images 5

Visual Enhancement Techniques For Underwater Images

引用

5th International conference on Advanced Computing and Communication systems (ICACCS)

作者： Lakshmi, Ch. Jaya Prasanti, K. Kalpana, S. VR Siddhartha Engn Coll Vijayawada India

ISBN: (纸本)9781538695333

In the recent days, wide range of research has been carried out on visual enhancement of under images in submarine and military operations to discover submerged structural designing and sea floor exploration. But, diving in the deep ocean for a long time has increased the difficulties for analysis of underwater images. Further, other factors such as scattering resulting from presence of particles inside the water and blurring effects reduce the quality of images being captured by underwater optic camera. There are several algorithms have been introduced to improve the visual quality of deep water images. Therefore, in this project, a novel algorithm based on bidirectional Empirical Mode Decomposition (BEMD) to enhance the visual quality of the underwater images will be implemented and comparison of data with conventional enhancement technique will be illustrated. The implementation will be done using MATLAB software.

关键词： Visual enhancement image processing Scattering effects Illumination conditions BEMD

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Design of human-like behavior learning machine based on neural network FPGA

Design of human-like behavior learning machine based on neur...

引用

2019 International conference on Applied Machine Learning and Data Science, ICAMLDS 2019

作者： Tang, Ming Hao, Shengli Xi'an Peihua University Xi'an China

Neural network has been widely used in image recognition, because it can imitate the behavior of biological visual nerve to obtain high recognition accuracy. Aiming at image recognition, this paper studies the machine self-learning system of image recognition results based on neural network FPGA, designs the data sensing and image recognition processing module based on neural network, and studies the key problems of the system self-recognition and machine self-learning process. The image recognition module uses bionics and parallax principle to get the synchronous exposure image of surrounding scenery, reconstruct the three-dimensional shape and position of surrounding objects, and get the information needed by the system design through image processing algorithm. On the basis of this module, a machine self-learning algorithm based on the result of image recognition is designed to build a knowledge base for the machine and guide the machine by modifying the weight and making decision to realize human-like behavior of machine self-learning. The experimental results show that the recognition accuracy of dynamic image recognition in image recognition network is about 80% compared with the corresponding static image recognition, and the time and power consumption are reduced by 24%, 27.8% and 41% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Knowledge based systems

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Real-Time Semantic Background Subtraction

Real-Time Semantic Background Subtraction

引用

IEEE International conference on image processing

作者： Anthony Cioppa Marc Van Droogenbroeck Marc Braham Department of Electrical Engineering and Computer Science (Montefiore Institute) University of Liége Belgium

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications. In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances. RT-SBS effectively combines a real-time background subtraction algorithm with high-quality semantic information which can be provided at a slower pace, independently for each pixel. We show that RT-SBS coupled with ViBe sets a new state of the art for real-time background subtraction algorithms and even competes with the non real-time state-of-the-art ones. Note that we provide python CPU and GPU implementations of RT-SBS at https://***/cioppaanthony/rt-sbs.

关键词： Semantics Real-time systems Change detection algorithms Heuristic algorithms image segmentation Prediction algorithms Classification algorithms

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Various image segmentation algorithms: A survey 2nd

Various image segmentation algorithms: A survey

引用

2nd International conference on Smart Computing and Informatics, SCI 2018

作者： Suresh, Kurumalla Srinivasa Rao, Peri Jawaharlal Nehru Technological University KakinadaA.P India CS&SE dept Andhra University College of Engineering VisakhapatnamA.P India

ISBN: (纸本)9789811319266

image segmentation is a necessary method in image processing. It is nothing but partitioned an image into several parts called segments. It has applications like image compression;because of this type of application, it is unable to develop the entire image. In that, time segmentation technique is used, to segment the portions from the image for remaining processing. Already certain methods are existed, which divides the single image into multiple parts depending on some constraints like intensity value of the pixel, image color, size, texture, etc. These methods can be divided based on segmentation method. In this paper, author reviewed some algorithms, and finally their pros and cons are listed. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image compression

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Stereo Matching Based on Improved Minimum Spanning Tree

Stereo Matching Based on Improved Minimum Spanning Tree

引用

International conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Yongxing Jia Kai Zhu Jing Yu Chuanzhen Rong Chao Li College of Communications Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9781728165172

ISBN: (纸本)9781728165189

The existing local stereo matching algorithms have some problems such as high noise sensitivity, low matching accuracy in occlusion regions, and long matching time, while the traditional Minimum Spanning Tree (MST) algorithm can simplify the cost aggregation step and improve the matching accuracy. However, for texture-less regions and edge regions, the matching effect of MST is still not ideal. In order to overcome these shortcomings, a stereo matching algorithm based on Improved Minimum Spanning Tree (IMST) is proposed in this paper. Firstly, based on the MST algorithm, our method transforms the original image into the image of minimum spanning tree, the cost aggregation of each tree node can be represented by the weight value and the matching cost of this node, and verifies the simplicity of the MST algorithm; Secondly, based on bilateral filtering, the grey similarity factor is redesigned to enhance the adaptive ability of the algorithm to the noise standard deviation to optimize the aggregation cost based on the MST; Thirdly, a separable bilateral filter is used to improve the processing speed while preserving the edge-preserving effect of the algorithm in the disparity refinement phase. Finally, based on the Middlebury Stereo Datasets, the pro-posed algorithm is compared with the Minimum Spanning Tree algorithm (MST) algorithm in three aspects: disparity map effect, matching accuracy and processing time. Compared with other algorithms, the proposed algorithm has better matching accuracy and shorter processing time.

关键词： Microsoft Windows Filtering algorithms image edge detection Matched filters Standards Information filtering

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Research on Distributed Parallel Computing Technology of Airborne Test Data

Research on Distributed Parallel Computing Technology of Air...

引用

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

作者： Lijun Yan Fenglei Shi Hongxiang Zhu Chinese Flight Test Establishment (CFTE) Xi’an China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

Aiming at the task requirements of a small fast-moving test vehicle with small space, short flight support interval and real-time task evaluation, at the same time, the future flight test is faced with many test parameters, wide distribution of test systems, and high real-time data requirements. To solve the rapid acquisition test evaluation data in a specific environment, we design distributed system, data analysis and fusion technology, embedded parallel computing and other technologies to realize the parallel computing and real-time processing on airborne data. The calculated data results are directly telemetry or recorded for real-time monitoring and rapid assessment of missions. The design meets the requirements of rapid real-time evaluation during the flight test and improves the flight test efficiency.

关键词： flight test distributed system parallel computation FLIGHT TESTS PARALLEL processing (COMPUTERS) Distributed systems observational data Test vehicles real-time process

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Vegetable Disease Detection Using K-Means Clustering And Svm

Vegetable Disease Detection Using K-Means Clustering And Svm

引用

International conference on Advanced Computing and Communication systems (ICACCS)

作者： U. Rahamathunnisa M.K. Nallakaruppan A. Anith Sendhil Kumar K.S. School of Information Technology and Engineering VIT Vellore India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. image processing technique is helpful in examination and evaluating the products. In this paper we proposed a vegetable disease detection system for recognizing diseased vegetables. Here we utilize the image processing system for reviewing the vegetables. Vegetables are recognized dependent on their features. The features are color, shape, size, texture. We extract these features utilizing algorithms to distinguish the vegetables. We develop a recognition system for 2D input images. The main aim of this work is detecting infected vegetable based on features with K-means clustering algorithm. Algorithm includes three main steps namely enhancement, segmentation and classification. Vegetable samples are collected as images from high resolution camera and data acquisition is carried out for database preparation. The image segmentation process is based on pixel of the image and is applied to get the segmented and infected vegetables using K-Means Clustering algorithm. The image classification is based on Support Vector Machine (SVM) which perform supervised leaning for classification.

关键词： Feature extraction Diseases image segmentation image color analysis Classification algorithms Clustering algorithms Support vector machines

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Research on Error Diffusion algorithms Based on Digital image Feature Matching 19

Research on Error Diffusion Algorithms Based on Digital Imag...

引用

19th IEEE International conference on Communication Technology, ICCT 2019

作者： Huan, Wang Peng, Cao Fangfang, Chen Wenqiu, Luo Beijing Institute of Graphic Communication School of Information Engineering Beijing China

ISBN: (纸本)9781728105352

In this paper, an improved algorithm of error diffusion is provided, which is better than the traditional error diffusion algorithm in processing image boundary, contour and texture details. It can make the outline of printed image more clear and display better under the same print equipment or print-arts conditions. Based on the original error diffusion algorithm, this algorithm extracts and enhances the edge contour and texture feature of halftone images, optimized the error diffusion algorithm. The result has the best matching of the display effect of the printed image in the smooth part and the edge contour part. © 2019 IEEE.

关键词： Textures

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Kernel Normalized Least Mean High Order Error Algorithm for Sub-Gaussian Noise

Kernel Normalized Least Mean High Order Error Algorithm for ...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Wei Gao Guoxuan Ma Lihuan Huang Lifan Zhang School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Marine Science and Technology Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781728144801

ISBN: (纸本)9781728144818

Robust kernel adaptive filtering (KAF) has been extensively studied to identify nonlinear system corrupted by the heavy-tailed impulsive noise, whereas few KAF algorithm is reported to concern on the light-tailed sub-Gaussian noise. This paper proposes a new kernel normalized least mean high order error (KNLMHOE) algorithm in the presence of sub-Gaussian noise. More importantly, the convergence condition on the mean stability of KNLMHOE is also provided. The KNLMHOE algorithm is simple to implement and exhibits significant performance improvement compared with several state-of-the-art KAF algorithms, which is confirmed by simulations.

关键词： Kernel Estimation error Dictionaries Convergence Nonlinear systems Approximation algorithms Adaptive systems

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