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

共 2695 条记录，以下是1751-1760 订阅

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Reconfiguration of Spiking neural Network for Optimization with applications to image processing

Reconfiguration of Spiking Neural Network for Optimization w...

引用

International conference on Emerging Trends in Engineering and Technology, ICETET

作者： Soni Chaturvedi A.A. Khurshid S.S. Dorle E&C Engg PIET Nagpur RCOEM Nagpur GHRCE Nagpur

This paper depicts the restructuring of different models of third generation of artificial neural network, that is, the spiking neural networks for image processing applications. The proposed work aims towards implementation of a novel algorithm using different models of Spiking neural networks which will improve upon the optimization results in the field of image processing. In this paper, we focus on various evaluation parameters like mean square error, mean absolute error peak signal to noise ratios as well as enhance the output using ANN as wellas Leaky Integrate and firing Model of Spiking neural networks.

关键词： image processing artificial neural networks Computational modeling Pattern recognition Market research Biological neural networks

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Application image processing to Predict Personality Based on Structure of Handwriting and Signature

Application Image Processing to Predict Personality Based on...

引用

2013 International conference on Computer, Control, Informatics and Its applications (IC3INA)

作者： Djamal, Esmeralda Contessa Darmawati, Risna Ramdlan, Sheldy Nur Univ Jenderal Achmad Yani Dept Informat Cimahi Indonesia

ISBN: (纸本)9781479910786

Handwriting stroke reflects how the author faced his world and the emotional honesty. By examining all elements of handwriting and interpreting them separately or integrated, we could generate a sketch of the writer's character traits, emotional disposition and social style using standard of graphology. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. In this research, using graphical approach based on a combination of signature and handwriting to predict the more personality using structure algorithms and multiple artificial neural networks (ANN). The image in A4 paper split into two areas: Signature area which nine features and handwriting based on five features. Each area had pre-processing performed to improve the recognition accuracy. Signature area is classified using ANN based on five features and using multi structure algorithms based on four features. While the handwriting area is classified using multi structure algorithm based on four features (margins, spacing between words and lines, and zone domination) and using ANN after hill valley extraction based on baseline features. Eight features are processed using multi-structure algorithms that provide 87-100% accuracy. In the meantime, six features are classified using an ANN which result an accuracy of 52-100%. It used 100 sets of data testing after training using back propagation with 25-75 data. The system has been implemented with the software so that it can be used for classification of personality from handwriting scanned automatically.

关键词： handwriting and signature analysis predict personality Graphology structure algorithms multiple multilayer perceptron

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Selective rotation-based CORDIC architecture for high-speed applications

引用

International conference on VLSI, Communication, Advanced Devices, Signals and Systems and Networking, VCASAN 2013

作者： Geetha, K.S. Jeyaram, Gayathri Bhagyashree, A. Electronics and Communication Department R.V. College of Engineering Mysore road R.V Vidyaniketan Post Bangalore 560059 India

ISBN: (纸本)9788132215233

The CORDIC algorithm is an efficient method for computing trigonometric, logarithmic, hyperbolic, and exponential functions. It is used when hardware multipliers are not available, mainly in FPGAs. It finds innumerable applications in digital communication, image processing, artificial neural networks, and robotics. In this paper, the conventional CORDIC algorithm is optimized using selective rotation (SR) techniques which employ rotation selection algorithm (RSA) for faster convergence. A coarse LUT (Look up Table) is incorporated in the design to obtain coarse values which are fine-tuned using SR CORDIC stages. This architecture is simulated and verified as a part of a numerically controlled oscillator (NCO) using MATLAB and SIMULINK. This novel architecture is area-efficient and reduces the number of iterations required, to converge to a value, by 50 %. © 2013 Springer.

关键词： Rotation

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Improving 3D Keypoint Detection from Noisy Data Using Growing neural Gas

Improving 3D Keypoint Detection from Noisy Data Using Growin...

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12th International Work-conference on artificial neural networks (IWANN)

作者： Garcia-Rodriguez, Jose Cazorla, Miguel Orts-Escolano, Sergio Morell, Vicente Univ Alicante Dept Comp Technol Alicante Spain Univ Alicante Inst Investigac Informat Alicante Spain

ISBN: (纸本)9783642386817

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

关键词： GNG Noisy Point Cloud Visual Features Keypoint Detection Filtering 3D Scene Registration

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Estimation of the RiiG-distribution parameters using the artificial neural networks

Estimation of the RiIG-distribution parameters using the Art...

引用

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

作者： Amar Mezache Izzeddine Chalabi Département d'Electronique Université de Constantine 1 Constantine Algeria Département d'Electronique Université de M'sila M'sila Algeria

In order to improve the estimation of the RiiG (Rician Inverse Gaussian) model parameters, the authors attempt to achieve the parameter estimates using the inverse function of the RiiG CDF (Cumulative Distributed Function) which the latter can not be obtained in a closed form. However, the ANN (artificial neural Network) technique is preferred which has the ability to approximate this nonlinear complexity. From recorded sea-clutter data, the regressions of the real CDF are used at the input layer of the ANN estimator. The weights of the network are optimized in real time by means of the genetic algorithm (GA) tool. The mean square error of estimates (MSE) criterion is considered to assess the estimation performance. For almost cases, the experimental results show that adopting the proposed scheme of the ANN estimator turns out the best parameter estimates and also allows a better matching of real CDF and real PDF (Probability density Function) than the standard IMLM (Iterative Maximum Likelihood Method) estimator.

关键词： artificial neural networks Estimation Clutter Radar conferences image processing Fitting

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Language and Automata Theory and applications: 7th International conference, LATA 2013, Bilbao, Spain, April 2-5, 2013, Proceedings

引用

2013年

作者： Adrian Horia Dediu Carlos Martn-Vide Bianca Truthe

ISBN: (纸本)9783642370656

This book constitutes the refereed proceedings of the 7th International conference on Language and Automata Theory and applications, LATA 2013, held in Bilbao, Spain in April 2013. The 45 revised full papers presented together with 5 invited talks were carefully reviewed and selected from 97 initial submissions. The volume features contributions from both classical theory fields and application areas (bioinformatics, systems biology, language technology, artificial intelligence, etc.). Among the topics covered are algebraic language theory; algorithms for semi-structured data mining; algorithms on automata and words; automata and logic; automata for system analysis and program verification; automata, concurrency and Petri nets; automatic structures; cellular automata; combinatorics on words; computability; computational complexity; computational linguistics; data and image compression; decidability questions on words and languages; descriptional complexity; DNA and other models of bio-inspired computing; document engineering; foundations of finite state technology; foundations of XML; fuzzy and rough languages; grammars (Chomsky hierarchy, contextual, multidimensional, unification, categorial, etc.); grammars and automata architectures; grammatical inference and algorithmic learning; graphs and graph transformation; language varieties and semigroups; language-based cryptography; language-theoretic foundations of artificial intelligence and artificial life; parallel and regulated rewriting; parsing; pattern recognition; patterns and codes; power series; quantum, chemical and optical computing; semantics; string and combinatorial issues in computational biology and bioinformatics; string processing algorithms; symbolic dynamics; symbolic neural networks; term rewriting; transducers; trees, tree languages and tree automata; weighted automata.

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Use an efficient neural network to improve the Arabic handwriting recognition

Use an efficient neural network to improve the Arabic handwr...

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

作者： Husam Ahmed Al Hamad Department of Information Technology Qassim University Qassim Saudi Arabia

Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.

关键词： image segmentation Training neural networks Feature extraction Accuracy Mathematical model Equations

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Brain imaging classification based On Learning Vector Quantization

Brain imaging classification based On Learning Vector Quanti...

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International conference on Communications, Signal processing, and their applications (ICCSPA)

作者： Baher H. Nayef Shahnorbanun Sahran Rizuana Iqbal Hussain Siti Norul Huda Sheikh Abdullah Pattern Recognition Research Group Center for Artificial Intelligence Technology FTSM Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Department of Radiology UKM Medical Center Universiti Kebangsaan Malaysia Kuala Lumpur Malaysia

The performance accuracy of the artificial neural Network (ANN) is highly dependent on the class distribution. Data multi-randomization before classification is proposed in this paper in order to obtain a proper classification model, which guaranties well performance of the classifiers. Multi randomization aims to allocate the best class distribution by re-ordering the input dataset randomly. In this paper, Learning Vector Quantization (LVQ) which is a supervised ANN, Multilayer perceptron (MLP), unsupervised Self organizing Map (SOM) and Radial Base Function (RBF) are used to classify multi randomized brain Magnetic Resonance Imaging (MRI) dataset. The proposed method showed significant improvement in the stability of the classifiers.

关键词： Magnetic resonance imaging Feature extraction image segmentation Accuracy artificial neural networks Training Neurons

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Mouth covered detection for yawn

Mouth covered detection for yawn

引用

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

作者： Masrullizam Mat Ibrahim John S Soroghan Lykourgos Petropoulakis Electronic and Electrical Engineering Department University of Strathclyde Glasgow Scotland United Kingdom

Yawn is one of the common fatigue sign phenomena. The common technique to detect yawn is based upon the measurement of mouth opening. However, the spontaneous human action to cover the mouth during yawn can prevent such measurements. This paper presents a new technique to detect the covered mouth by employing the Local Binary Pattern (LBP) features. Subsequently, the facial distortions during the yawn process are identified by measuring the changes of wrinkles using Sobel edges detector. In this research the Strathclyde Facial Fatigue (SFF) database that contains genuine fatigue signs is used for training, testing and evaluation of the developed algorithms. This database was created from sleep deprivation experiments that involved twenty participants.

关键词： Distortion measurement artificial neural networks

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Microscobic imaging with automatic scanner

Microscobic imaging with automatic scanner

引用

IEEE Signal processing and Communications applications (SIU)

作者： Hülya Yaldız Murat Ekinci Bilgisayar Mühendisliği Bölümü Karadeniz Teknik Üniversitesi Trabzon Turkey

First and fundamental function to make an automatic image analysis on microscobic images is automatic implementation of imege sharpness before scanning function of sample, all area of which is analyzed, by scanning on the X-Y-Z plane and automatic continuation of image sharpness during the image scanning process. When automatic scanning, for bacterial analyses, within the boundaries of the sample area on the X-Y plane, automatic detection of outside field area from image sequence is one of the other fundamental functions. Implementation and protection of focus in imaging on all the stage of of the sample area scanning, on the Z axis direction, must be provided. In this work, for imaging provided with CMOS camera from light microscopy motorized system making scanning process automatically by detecting the boundaries of the sample area with visual data on X-Y-Z plane, automatic focus and the success of auto-focusing continuity during scanning are presented. Focusing microscopic imaging is based on optimizing based on analyses of variance image sequences distance in the Z axis direction between lens and sample. After determination of optimization for imaging with automatic focusing in the starting position platform, which placed the sample on, is moved in the X-Y plane for scanning. Focus distortion caused by possible distance changes on the Z axis during scanning process of this movement is automatically detected. image enhancement for which in low degree of distortion is provided with software, and for which high level is provided by success of auto control of platform motion, in Z axis direction. Review of in/out the field based on multi-layer artificial neural network to detect coming to the limit of scan area is succeed with processing of image sequences.

关键词： Microscopy image sequences CMOS integrated circuits Focusing artificial neural networks Wiener filters

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