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

共 16347 条记录，以下是4821-4830 订阅

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image Radar-based Traffic Surveillance System: An all-weather sensor as intelligent transportation infrastructure component

Image Radar-based Traffic Surveillance System: An all-weathe...

引用

International SoC Design conference, ISOCC

作者： Yu Du Ka Lok Man Eng Gee Lim School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9781728183312

ISBN: (纸本)9781728183329

Sensing, processing, and communication are the 3 key elements for Intelligent Transportation systems (ITS), while processing is ever advancing on cloud and communication that seems to be solved already by the implementation of 5G communication protocol, sensing has become the most critical part. Traditional video dominated sensing system needs revolutions because of many physical limitations such as degraded performance under bad weather and low illumination conditions, incompetent of detection and tracking overlapped objects, deficient distance and speed detection ability as well as limited field of view. Thankfully, these limitations can be well compensated by radar technology. Radar is known as a kind of all-weather sensor with high accuracy and long-range sensing capability, a radar video fused sensing system could be the key to the next level of intelligent transportation system.

关键词： Surveillance Radar Radar imaging Traffic control Radar tracking Sensors Intelligent transportation systems

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Hyperspectral image classification based on pre-post combination process 6

Hyperspectral image classification based on pre-post combina...

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6th International conference on systems and Informatics, ICSAI 2019

作者： Luo, Kaiqing Qin, Yang Yin, Dan xiao, Hua School of Physics and Telecommunication Engineering South China Normal University Guangzhou Guangdong China

ISBN: (纸本)9781728152561

Aiming at the problem of poor classification accuracy of traditional machine learning algorithms based on spectral information analysis, this paper proposes a hyperspectral image classification method based on pre-processing before classification and processing optimization combination after classification. Firstly, the original samples are subjected to Gaussian filter and Linear discriminant analysis for reducing noise and dimensions. Then, the data is initially classified by traditional machine learning algorithms such as k-nearest neighbor(KNN), Support Vector Machine (SVM), sparse representation-based classifier (SRC)or multiple logistic regression (MLR). Combining local pixel spatial information to determine the confidence of the prediction labels. Finally, the initial prediction label is corrected by a continuous multi-layer neighborhood optimization layers to obtain a final classification label. Comparative experiments were performed on multiple hyperspectral remote sensing databases such as Indian Pines. The experimental results show that the proposed method has obvious performance improvement in classification accuracy and time efficiency, which has a certain degree of robustness in the process of combining with different classifiers. © 2019 IEEE.

关键词： image classification

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Progress and challenges in compressible and learnable image encryption for privacy-preserving image encryption and machine learning [keynote]

Progress and challenges in compressible and learnable image ...

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International conference on Knowledge and Smart Technology (KST)

作者： Hitoshi Kiya Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9781728144665

ISBN: (纸本)9781728144672

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. With the wide/rapid spread of distributed systems for information processing, such as cloud computing and social networking, not only transmission but also processing is done on the intemet. However, cloud environments have some serious issues for end users, such as unauthorized access, data leaks, and privacy compromise, due to unreliability of providers and some accidents. Accordingly, we first focus on compressible image encry ption schemes, which have been proposed for encryption-then-compression (EtC) systems, although the traditional way for secure image transmission is to use a compression-then encry ption (CtE) system. EtC systems allow us to close unencrypted images to network providers, because encrypted images can be directly compressed even when the images are multiply recompressed by providers. Next, we address the issue of leamable encryption. Huge training data sets are required for machine leaming and deep leaming algorithms to obtain high performance. However, it requires large cost to collect enough training data while maintaining people's privacy.

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Computer vision based automated billing system for fruit stores

Computer vision based automated billing system for fruit sto...

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International conference on Smart systems and Inventive Technology (ICSSIT)

作者： M Sugadev Kudapa Sucharitha I.Rexline Sheeba Balamurugan Velan Sathyabama Institute of Science and Technology Chennai

ISBN: (数字)9781728158211

ISBN: (纸本)9781728158228

This paper describes about the new billing system which is more accurate and time efficient than conventional billing system used in fruit retail shops. The proposed method uses the deep learning neural network approach for classification of fruits and strain gauge type load cell to estimate the weight of the fruit items kept in the basket. The neural network takes input image in the form of clusters and this cluster forms a centroid that is processed to classify fruits like Apple, Banana, Granny Smith, etc. The image processing and weight computation algorithms are implemented in RaspberryPi board with tensor flow and openCV library functions. The proposed system automatically recognize the type of fruits, finds the weight of the fruit in grams, computes the total cost and prints the bill statement.

关键词： Machine learning Computer vision Tensile stress Weight measurement image processing Libraries Neural networks

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A Neural Network based Attendance Monitoring and Database Management System using Fingerprint Recognition and Matching

A Neural Network based Attendance Monitoring and Database Ma...

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2019 IEEE International conference on Electronics, Computing and Communication Technologies, CONECCT 2019

作者： Singh, Seema BMS Institute of Technology and Management Dept. of Electronics and Telecommunication Engg Bengaluru India

ISBN: (数字)9781728124728

ISBN: (纸本)9781728124728

Most authentication systems use fingerprints for identification. The uniqueness of fingerprint for each individual forms the basis of faultless identification. However, the image generated by the scanner may give varying results during each scan which thereby results in a major drawback in most existing systems. Thus, this paper involves the implementation of robust portable Neural Network based system to provide an efficient matching algorithm for fingerprint authentication systems. image processing algorithms and appropriate choice of features for the training of neural networks is presented. The system is implemented on Raspberry Pi with appropriate interfacing modules to make the system standalone. As the proposed system is portable, it can be used as an attendance monitoring system in classrooms. The back-end system involves the processes of image acquisition and processing to create a suitable database. The corresponding hardware model is created in MATLAB and then deployed in the Raspberry Pi-3 module to form a standalone system. © 2019 IEEE.

关键词： image enhancement

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Satellite image Classification for Detecting Unused Landscape using CNN

Satellite Image Classification for Detecting Unused Landscap...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： S. Akshay T. K. Mytravarun N. Manohar M. A. Pranav Amrita Vishwa Vidyapeetham Mysore India

ISBN: (数字)9781728141084

ISBN: (纸本)9781728141091

As the landscapes changes day by day it leads to the increasing use of unused lands, by which unused lands can be used for various purposes like agriculture, developing city infrastructure and many more. This paper helps in automating the process of detecting the unused land space. In this work, a system for satellite image processing that detects unused land is proposed. Here remote sensing earth images are taken as the dataset where the pre-processing step includes converting image into greyscale image, compression and noise removal. Segmentation is done to partition the region of used and unused lands. Feature extraction is done here using local binary feature extraction in-order to identify edge, flat and corner surfaces. As the mentioned various algorithm is used in classification and labeling of remote sensing earth images. CNN algorithm is also used for classification and labeling of classification is done automatically by the use of CNN algorithm. Random forest is used to segregate two landscapes as used and unused land which gives accuracy better than the existing systems.

关键词： Satellites Classification algorithms image segmentation Feature extraction image edge detection Spatial resolution Remote sensing

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Using Algorithm Selection for Adaptive Vehicle Perception aboard UAV 16

Using Algorithm Selection for Adaptive Vehicle Perception ab...

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16th IEEE International conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Hellert, Christian Koch, Simon Stuetz, Peter Univ Bundeswehr Munich Inst Flight Syst Neubiberg Germany

ISBN: (纸本)9781728109909

Surveillance sensors aboard UAV are affected by environmental influences, e.g. atmospheric or topographic factors. This paper proposes a method for the automatic adaption of airborne sensor applications such as street surveillance to changing environmental conditions, preventing overall performance degradation with minimum human intervention. The basic principle of the concept relies on the selection of the most appropriate data processing algorithm available on board. To facilitate the determination of the most effective algorithm, performance models are used to predict the expected suitability of each algorithm for the given environmental conditions. Modeling the relation between the environmental state and the performance of the algorithms is achieved by two approaches leveraging expert knowledge and machine learning methods. An evaluation was carried out in simulation as well as in real flight experiments showing that the proposed method is able to improve overall vehicle perception performance.

关键词： Data models Prediction algorithms Adaptation models image sensors Sensor systems Data processing

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An investigation on digital signal processing for coherent optical communication system with advanced modulation format

An investigation on digital signal processing for coherent o...

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Electrical Engineering and Control Technologies (CEECT), conference on

作者： Simiao Huang Xidian University Xi’an China

ISBN: (数字)9781728197975

ISBN: (纸本)9781728197982

In recent years, many new applications such as cloud computing, Internet of Things (IoT), artificial intelligence (AI) are developing rapidly, which put large capacity requirements on communication network. Coherent optical communication systems based on advanced modulation format have been extensively studied to improve transmission capacity. High-order modulation format requires a better signal-to-noise ratio (SNR) for signal receiving. However, the various damages in transmission links would decrease SNR. In this paper, digital signal processing (DSP) techniques are investigated to compensate signal damages, including Time-domain equalization (TDE) algorithm to compensate chromatic dispersion (CD), constant mode algorithm (CMA) and direct decision least mean square (DD-LMS) algorithm to compensate polarization mode dispersion (PMD), fourth power algorithm to correct frequency deviation, Viterbi & Viterbi and Blind Phase Search (BPS) algorithms to estimate phase noise. Moreover, constellation shaping technology is also discussed to make the transmission capacity closer to Shannon limit.

关键词： Viterbi algorithm Signal processing algorithms Digital signal processing Optical fiber communication Internet of Things Artificial intelligence Signal to noise ratio

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Analysis of Segmentation Techniques for Synthetic Aperture RADAR image processing Applications

Analysis of Segmentation Techniques for Synthetic Aperture R...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： Pratibha Singh Jaiswal Mahipal Singh Choudhry Delhi Technological University Delhi India

ISBN: (数字)9781728141084

ISBN: (纸本)9781728141091

Synthetic aperture RADAR (SAR) is an emerging technology primarily used for high resolution ground mapping. SAR's capability to perform under adverse weather conditions, day or night makes it advantageous for many civilian and military applications. It is an active imaging system and inherently suffers from speckle noise, which is multiplicative in nature. Speckle noise originates from the interferen'ce of the coherent wave fronts associated with the reflected signal. The quality of SAR images is significantly degraded by this interference. Conventional intensity-based image processing techniques do not perform well for SAR images because of multiplicative nature of speckle. A variety of suggested image segmentation techniques suitable for SAR images have been analysed in this paper. The paper performs comparative analysis of image segmentation techniques that are based on different features of the images and are specialized to perform on SAR images. Experiments are performed on synthetically generated images and real SAR images obtained from Moving and Stationary Target Acquisition and Recognition (MSTAR) public database.

关键词： image segmentation Synthetic aperture radar Entropy Speckle Clustering algorithms image resolution conferences

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image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications: A Comparison Study 12th

Image Enhancing in Poorly Illuminated Subterranean Environme...

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12th International conference on Computer Vision systems (ICVS)

作者： Kanellakis, Christoforos Karvelis, Petros Nikolakopoulos, George Lulea Univ Technol Dept Comp Elect & Space Engn Robot Team S-97187 Lulea Sweden

ISBN: (纸本)9783030349950;9783030349943

This work focuses on a comprehensive study and evaluation of existing low-level vision techniques for low light image enhancement, targeting applications in subterranean environments. More specifically, an emerging effort is currently pursuing the deployment of Micro Aerial Vehicles in subterranean environments for search and rescue missions, infrastructure inspection and other tasks. A major part of the autonomy of these vehicles, as well as the feedback to the operator, has been based on the processing of the information provided from onboard visual sensors. Nevertheless, subterranean environments are characterized by a low natural illumination that directly affects the performance of the utilized visual algorithms. In this article, an novel extensive comparison study is presented among five State-of the-Art low light image enhancement algorithms for evaluating their performance and identifying further developments needed. The evaluation has been performed from datasets collected in real underground tunnel environments with challenging conditions from the onboard sensor of a MAV.

关键词： Low light imaging image enhancement Subterranean MAV applications

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