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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2668 条记录，以下是1821-1830 订阅

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Determining effective colour components for skin detection using a clustered neural network

Determining effective colour components for skin detection u...

引用

IEEE International conference on Signal and image processing applications (ICSIPA)

作者： Sepideh Araban Fardad Farokhi Kave Kangarloo Member of Scientific Association of Electrical and Electronic Engineering Central Tehran Branch Islamic Azad University Tehran Iran Electrical Engineering Department Central Tehran Branch Islamic Azad University Tehran Iran

Object detection problems-skin detection here can be considered as object recognition problems with two classes. In this paper, each given class is clustered using the Kmeans algorithm into multiple subclasses and a Multilayer perceptron (MLP) neural network (NN) is trained for each clusters separately. In the testing phase, each point is compared with centers of clusters and the network related to closest center is selected for each new cluster. Besides the system performance improvement, it also can significantly reduce the testing time. Then the Utans algorithm as a trained NNs-based feature selection method is applied to 44 color components of 15 different color spaces. The obtained results show that the presented algorithm compare to other algorithms has higher performance and less execution time as well.

关键词： image color analysis Skin artificial neural networks Clustering algorithms Lighting Training Histograms

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Multi-receiver modulation classification for satellite communications signals

Multi-receiver modulation classification for satellite commu...

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IEEE International conference on Signal and image processing applications (ICSIPA)

作者： M. Farhang H. Dehghani H. Bahramgiri Department of Electrical and Electronics Engineering Malek-e-Ashtar University of Technology (MUT) Tehran Iran

In order to improve the performance of modulation classification systems, the idea of multi-receiver recognition has been developed recently. In this paper multiple receivers' collaboration at different information levels is investigated for classification of signals used in DVB-S2 standard. Three methods are proposed for receivers' cooperation at each one of signal, feature and decision levels. The proposed methods use cumulants and MLP neural network as signal features and classifier respectively. These methods are evaluated and compared through performance, complexity and equipment. The results show that receivers' cooperation at signal level offers more accurate classification compared to feature and decision levels, in addition to less computational complexity.

关键词： Modulation Receivers Feature extraction Computational complexity artificial neural networks Digital video broadcasting AWGN

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Stereoscopic Modeling of Building Rooftop from IKONOS Satellite image Data

Stereoscopic Modeling of Building Rooftop from IKONOS Satell...

引用

International conference on Information Science and applications (ICISA)

作者： Dong-Min Woo Dong-Chul Park Image Processing Laboratory Department of Electronics Engineering Myoneji University Gyeonggi South Korea

ISBN: (纸本)9781424492220

This paper presents a novel approach for the generation of 3D building model from IKONOS satellite image data. The main idea of 3D modeling is based on the grouping of 3D line segments. The divergence-based centroid neural network is employed in the grouping process. Prior to the grouping process, 3D line segments are extracted with the aid of the elevation information obtained by using area-based stereo matching of satellite image data. High-resolution IKONOS stereo images are utilized for the experiments. The experimental result proved the applicability and efficiency of the approach in dealing with 3D building modeling from high-resolution satellite imagery.

关键词： Three dimensional displays Buildings image segmentation artificial neural networks Satellites Neurons image reconstruction

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A Three Dimensional Bi-directional Self-Organizing neural Network (3-DBDSONN) Architecture for Three Dimensional Object Extraction from a Noisy Perspective

A Three Dimensional Bi-directional Self-Organizing Neural Ne...

引用

International conference on Emerging applications of Information Technology (EAIT)

作者： Kousik Dasgupta Siddhartha Bhattacharyya Paramartha Dutta Jyotsna Kumar Mandal Department of CSE Kalyani Government Engineering College Kalyani India Department of CSE & IT University Institute of Technology University of Burdwan Burdwan India Department of CSS Visva Bharati University Santiniketan India Department of CSE Kalyani University Kalyani India

A novel three dimensional bi-directional self-organizing neural network (3-DBDSONN) architecture with fuzzy context sensitive thresholding suitable for three-dimensional (3D) image processing applications, is proposed in this article. The proposed architecture comprises three interconnected three-dimensional fuzzy layers of neurons. It is devoid of any back-propagation algorithms for weight adjustments. An application of extraction of three-dimensional binary objects from various degrees of noisy background is demonstrated on one synthetic and two real life three-dimensional binary voxelized images. The efficiency of the proposed network as regards to immunity to different types of noise indicates encouraging avenues.

关键词： Three dimensional displays Neurons Computer architecture image processing artificial neural networks Noise measurement Bidirectional control

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An alternative method for cell counting

An alternative method for cell counting

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IEEE Signal processing and Communications applications (SIU)

作者： Akın Özkan S. Belgin İşgör Hakan Tora Pembegül Uyar Mesude İşcan Elektrik ve Elektronik Mühendisliği Bölümü ATILIM Üniversitesi Turkey Kimya Mühendisliğive Uygulamalı Kimya Bölümü ATILIM Üniversitesi Turkey Biyolojik Bilimler Bölümü ORTA DOğU TEKNİK Üniversitesi Turkey

Cell counts and classification of the cells play an important role in the field of microbiology and cell biology. Although there exists many counting processes for cells of interest in suspension, the most basic cell counting process is performed by a person via the microscope. For counting cells the simplest, widely used and the most economic method is the use of hemocytometer counting. In this study, the hemocytometer counting was used but the the cells were counted by a proposed image based approach. The developed technique herein uses neural network along with the Hough transform.

关键词： Signal processing conferences artificial neural networks Biomedical imaging Economics Digital images Companies

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Empirical modeling using symbolic regression via postfix Genetic Programming

Empirical modeling using symbolic regression via postfix Gen...

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International conference on image Information processing

作者： Vipul K. Dabhi Sanjay K. Vij Information Technology Department Dharmsinh Desai University Nadiad Gujarat India Sardar Vallabhbhai Patel Institute of Technology Sardar Vallabhbhai Patel Institute of Technology Vasad Gujarat India

Developing mathematical model of a process or system from experimental data is known as empirical modeling. Traditional mathematical techniques are unsuitable to solve empirical modeling problems due to their nonlinearity and multimodality. So, there is a need of an artificial expert that can create model from experimental data. In this paper, we explored the suitability of neural Network (NN) and symbolic regression via Genetic Programming (GP) to solve empirical modeling problems and conclude that symbolic regression via GP can deal efficiently with these problems. This paper aims to introduce a novel GP approach to symbolic regression for solving empirical modeling problems. The main contribution includes: (i) a new method of chromosome representation (postfix based) and evaluation (stack based) to reduce space-time complexity of algorithm (ii) comparison of our approach with Gene Expression Programming (GEP), a GP variant (iii) algorithms for generating valid chromosomes (in postfix notation) and identifying non-coding region of chromosome to improve efficiency of evolutionary process. Experimental results showed that empirical modeling problems can be solved efficiently using symbolic regression via postfix GP approach.

关键词： Biological cells Mathematical model artificial neural networks Neurons Data models Information processing Equations

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Wildfire smoke detection using computational intelligence techniques

Wildfire smoke detection using computational intelligence te...

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IEEE International conference on Computational Intelligence for Measurement Systems and applications (CIMSA)

作者： Angelo Genovese Ruggero Donida Labati Vincenzo Piuri Fabio Scotti Department of Information Technology Università degli Studi di Milano Milan Italy

In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.

关键词： Feature extraction image color analysis Delta modulation artificial neural networks Shape Machine vision image edge detection

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Informatics Engineering and Information Science, Part iiI 2011

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丛书名： Communications in Computer and Information Science

2011年

作者： Azizah Abd Manaf Shamsul Sahibuddin Rabiah Ahmad Salwani Mohd Daud Eyas El-Qawasmeh

ISBN: (数字)9783642254628

ISBN: (纸本)9783642254611

This 4-Volume-Set, CCIS 0251 - CCIS 0254, constitutes the refereed proceedings of the International conference on Informatics Engineering and Information Science, ICIEIS 2011, held in Kuala Lumpur, Malaysia, in November 2011. The 210 revised full papers presented together with invited papers in the 4 volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on e-learning, information security, software engineering, image processing, algorithms, artificial intelligence and soft computing, e-commerce, data mining, neural networks, social networks, grid computing, biometric technologies, networks, distributed and parallel computing, wireless networks, information and data management, web applications and software systems, multimedia, ad hoc networks, mobile computing, as well as miscellaneous topics in digital information and communications.

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Supervised Learning Framework For Screening Nuclei in Tissue Sections

Supervised Learning Framework For Screening Nuclei in Tissue...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society

作者： Kaustav Nandy Prabhakar R. Gudla Ryan Amundsen Karen J. Meaburn Tom Misteli Stephen J. Lockett Optical Microscopy and Analysis Laboratory Advanced Technology program SAIC-Frederick Inc. Electrical Engineering and Computer Science Department University of Michigan Cell Biology of Gene Expression National Cancer Institute National Institutes of Health

ISBN: (纸本)9781424441211

Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.

关键词： tissue section Cell nucleus Semi-supervised Learning Nucleus Dataset image analysis MULTISTAGE artificial neural networks Frameworks nuclei (nuclear physics) segmentation Segmentation Automation pattern classification method

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ANN-based Control Method Implemented in a Voltage Source Converter for Industrial Micro-grid

ANN-based Control Method Implemented in a Voltage Source Con...

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International conference on Bio-Inspired Computing: Theories and applications, BICTA

作者： Jinbang Xu Zhizhuo Wu Xuan Yang Jie Ye Anwen Shen Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science & Technology Huazhong University of Science & Technology Wuhan China

With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWM-VSC) have been investigated. Conventional PI controller has shown limitations such as sensitivity to load and system parameter variation. Even the stability of the system can be threatened under a large and sudden load change. In this paper, the practical situation of a VSC for industrial Micro Grid (MG) is considered and an artificial neural network (ANN) based control method is employed to solve the problem. Meanwhile, an on-line parameter tuning algorithm is introduced for its advantage of self-tuning and system character identification. The proposed control scheme is verified through simulation based on SABER software. The simulation results have shown the advantage of the proposed method and the performance of the parameter tuning session.

关键词： Voltage control artificial neural networks Power conversion Regulators Voltage measurement Frequency control

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