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检索条件"主题词=Image Edge Detection"

共 27763 条记录，以下是91-100 订阅

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Implementation of Neuro-Fuzzy System based image edge detection

Implementation of Neuro-Fuzzy System based image edge detect...

引用

IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Manel Elloumi Mohamed Krid Dorra Sellami Masmoudi University of Sfax Sfax Engineering School Sfax Tunisia

In this paper, we propose an implementation of a Neuro-Fuzzy System (NFS) with on chip learning for achieving different image processing tasks such as filtering, edge detection, etc. The complexity of this kind of implementation makes the pulse mode an important approach to achieve our goal thanks to its higher density of integration. As validation example, we propose here the edge detection process to be approximated by this system. The proposed system has proven a good approximation ability with a reduced neuron number and learning time cost. Moreover, the efficiency of our proposed system versus conventional edge detection operators is demonstrated. For different error criteria, our design shows the lowest values. The designed system is implemented on a field-programmable gate array (FPGA) platform. Synthesis results prove that the implemented NFS provides the best compromise between compactness, speed and accuracy compared to previous works from literature.

关键词： image edge detection Hardware Educational institutions Field programmable gate arrays System-on-chip Clocks

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An image edge detection Algorithm Based on Local Entropy

An Image Edge Detection Algorithm Based on Local Entropy

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IEEE International Conference on Integration Technology (ICIT)

作者： Wenzhan Dai Kangtai Wang College of Information And Electronic Engineering Zhejiang Gongshang University Hangzhou Zhejiang China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou Zhejiang China

A new algorithm of detecting image edge based on local entropy is proposed. It is analyzed that the smaller the local entropy is, the bigger the dispersion is. So the point with big local entropy is considered as a edge pixel, which can be detected by computing the local entropy of pixel and its eight neighbor pixel. Simulation shows that this algorithm has a higher precision because of having better anti-noise ability.

关键词： image edge detection Entropy Pixel Smoothing methods Educational institutions Laplace equations Change detection algorithms Computational modeling Pattern recognition Machine vision

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Comparison of Approaches ofDistributed Satellite image edge detection on Hadoop

Comparison of Approaches ofDistributed Satellite Image Edge ...

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International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU)

作者： Tapan Sharma Vinod Shokeen Sunil Mathur Research Scholar ASET Amity University Noida Uttar Pradesh India ASET Amity University Noida Uttar Pradesh India MAIT GGS Indraprastha University Delhi India

ISBN: (纸本)9781538638330

In the world of bigdata, Hadoop has become the major platform for storage and processing. Due to advancement in technology in the area of remote sensing, tremendous growth in data has been witnessed. In this paper, we have conducted experiments and compared two methods in which edge detection of satellite images is performed on Hadoop. Since, edge detection is one of the prime steps in the field of image processing and is being used for object detection in the image, we have targeted this basic algorithm of image processing for our experiments. In earlier research for edge detection on Hadoop, SequenceFiles were used to store and process images. In our experiments, we have leveraged distributed processing of Hadoop by logically splitting the file on HDFS and performing edge detection in distributed manner. The experiments were performed on Amazon AWS Elastic MapReduce (EMR) cluster using different satellite images varying from 10MB-200MB. The paper describes the comparison of the two approaches.

关键词： image edge detection Satellites Task analysis Distributed processing Clustering algorithms Remote sensing

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Determination of Shelled Corn Damages using Colored image edge detection with Convolutional Neural Network

Determination of Shelled Corn Damages using Colored Image Ed...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Analyn Yumang Glenn Magwili Sev Kyle C. Montoya Corleone Jorel G. Zaldarriaga School of EECE Mapúa University Intramuros Manila Philippines

ISBN: (纸本)9781665429979

In the Philippines corn is one of the top agricultural products produced in the country, specifically yellow corn. It is distributed in various cities and provinces to consumers. It is important that the corn kernels to undergo quality assurance before releasing them to the consumers. The methods for evaluating and qualifying corn kernels that are employed by most farms in the country are only done by manual human inspection and these methods are inconsistent which results to inaccurate findings. This is more prevalent when dealing with large amounts of kernels that need to be qualified. This study offers to reduce those inconsistencies by implementing a neural network-assisted method of inspection. The damages to corn kernels can be determined by its physical attributes and as such, the neural network will easily detect the type of damage within a given sample. Aside from the healthy kernels, the types of damage that was included in this study are the following: drier damage, heat damage, heat damage (drier phase), OCOL (Other Color) Type A and OCOL Type B. The neural network that will be used will be a Convolutional Neural Network wherein the images of the samples are subjected to layers of processing. This study also uses Colored image edge detection. The detection method used in this study has obtained an accuracy rating of 96.66%.

关键词： Training Heating systems image edge detection Neural networks Inspection Convolutional neural networks Kernel

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An Algorithm of image edge detection Based on Wavelet Transform

An Algorithm of Image Edge Detection Based on Wavelet Transf...

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Yue Wu Xiaohong Meng Institute of Earthquake Science China Earthquake Administration Key Laboratory of Geo-detection

image edge structure is often the most important feature in image processing and pattern recognition, and edge detection is mainly used to measure, detect and locate the gray level changeWavelet analysis is a kind of multi scale edge detection method which has excellent denoising ability and complete edge detection abilityUsing quadratic B-spline strip as operator, using edge detection algorithm based on wavelet analysis and dealing with the characteristics of texture image and heartbeat advantages and wavelet transform for image detail description of organic unifies in together, get the edge image quality is better.

关键词： Wavelet transform image edge detection Multi-scale edge

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Color image edge detection based on nonparametric density estimation

Color image edge detection based on nonparametric density es...

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IEEE International Conference on image Processing

作者： G. Economou A. Fotinos S. Makrogiannis S. Fotopoulos Department of Physics Electronics Laboratory University of Patras Patras Greece

A novel computationally efficient adaptive algorithm to accomplish edge detection in multidimensional and color images is presented. It is a statistical approach, based on local, non-parametric kernel density estimation. The location of the edge discontinuity coincides with the image density function minimum and it is determined by appropriate resampling of the locally defined probability space. The operator is radially symmetric and can be easily adapted to cope with signals of any dimensionality.

关键词： Color image edge detection Multidimensional systems Kernel Probability image processing Laboratories Physics computing Adaptive algorithm Density functional theory

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Ant colony optimization variants in image edge detection

Ant colony optimization variants in image edge detection

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International Conference on Communications and Signal Processing

作者： Susmita Koner Sriyankar Acharyya West Bengal University of Technology West Bengal India

ISBN: (纸本)9781479933594

edges in an image are the curves consisting of pixels wherein both side contains pixels with non-uniform intensity. edge detection is a part of low level image processing, much needed in various fields. Though edge detection can be done by various derivative techniques but it can also be detected well using meta-heuristic approximation algorithms. Ant Colony Optimization (ACO) is such a meta-heuristic technique to solve it. In basic ACO which comprises five phases: Initialization, Construction, Updation, Decision and Visualization, we have proposed and implemented total eight variations in this paper by modifying initialization and construction phase. In the initialization phase we have given a constraint in one variant that ants will be initialized near to edge to eliminate useless construction steps and unwanted edge detection where the other variant is without this constraint which may generate unnecessary edges in the resulting image. We have taken other two variations in selecting the next pixel in the construction phase: in one Greedy method is used, in another Roulette wheel selection method is used. Apart from these, in this phase two more variations have been done depending on memory size of ants i.e. applying tabu list memory of ants and ants without memory. Hence on the basis of two types of selection method used, two types of memory size of ants and two types of initialization phase, we have implemented eight variations individually in this paper. We observe that the variant, with roulette wheel selection, incorporated with the tabu list memory of ants, and with the new initialization condition outperforms others.

关键词： image edge detection Optimization Annealing Visualization Indexes

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A Fast image edge detection Algorithm Based on Row-Column Scanning

A Fast Image Edge Detection Algorithm Based on Row-Column Sc...

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Congress on image and Signal Processing, CISP

作者： Q. H. Wang A. P. Yang W. Z. Dai Department of Automatic Control Zhejiang University of Science and Technology Hangzhou China Department of Electron & Electric Longyan University Longyan China Zhejiang University of Finance and Economics Hangzhou China

A new algorithm of image edge detection is proposed based on analysis that there are three parts can be improved in the classical method of image edge detection. A novel algorithm is introduced in detail and the image edge is respectively detected in row and column direction. The flow chart of algorithm is given. In the end, the algorithm is applied in some test systems and results show it can effectively detect complicated image edge with higher quality and fast speed compared with other detection methods.

关键词： image edge detection Arithmetic image analysis Feature extraction Signal processing algorithms Electrons Finance Algorithm design and analysis Flowcharts System testing

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Design and implementation of video image edge detection system based on FPGA

Design and implementation of video image edge detection syst...

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Congress on image and Signal Processing, CISP

作者： Jincheng Wu Jingrui Sun Wenying Liu College of Physics & Electronic Information Science Tianjin Medical University China

The purpose of this paper is to describe the difference between edge detection based on software and the full use of hardware resources within the FPGA. According to the video signal theory and edge detection algorithm, the author designs the IP core of video image edge detection and builds the video image edge detection system on the EDK. The hardware circuit of the algorithm is achieved on ISE9.1 and simulated in Model Sim 6.0. When the system is validated, it indicates that the video image edge detection system can detect high-precision edge.

关键词： image edge detection Pixel Streaming media Field programmable gate arrays Laplace equations Convolution Decoding

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An Application of CP Neural Network Based on Rough Set in image edge detection

An Application of CP Neural Network Based on Rough Set in Im...

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IEEE International Conference on Granular Computing (GRC)

作者： Min Dong HuiYu Jiang XiangPeng Li Qing Liu College of Science Wuhan University of Science and Engineering Wuhan China Institute of Chemical and Engineering Wuhan University of Science and Engineering Wuhan China Department of Computer Science and Engineering University of Nanchang Nanchang China

Based on the rough set theory, a counter propagation neural network algorithm for edge detection is presented in this paper. Firstly, a definition of rough membership function, which is used to modify the weigh values in the nomal counter propagation neural network, is proposed after introducing the rough set. Experiments show that the approach has achieved good results in improving the accuracy of detection. And this algorithm can also overcome effectively the problem of simply cluster in the nomal counter propagation neural network.

关键词： Neural networks image edge detection Counting circuits Signal processing algorithms Set theory Educational institutions Computer applications Computer networks Physics computing Chemical engineering

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