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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1811-1820 订阅

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Select and sample - A model of efficient neural inference and learning

Select and sample - A model of efficient neural inference an...

引用

25th Annual conference on neural Information processing Systems 2011, NIPS 2011

作者： Shelton, Jacquelyn A. Bornschein, Jörg Sheikh, Abdul-Saboor Berkes, Pietro Lücke, Jörg Frankfurt Institute for Advanced Studies Goethe-University Frankfurt Germany Volen Center for Complex Systems Brandeis University Boston United States

ISBN: (纸本)9781618395993

An increasing number of experimental studies indicate that perception encodes a posterior probability distribution over possible causes of sensory stimuli, which is used to act close to optimally in the environment. One outstanding difficulty with this hypothesis is that the exact posterior will in general be too complex to be represented directly, and thus neurons will have to represent an approximation of this distribution. Two influential proposals of efficient posterior representation by neural populations are: 1) neural activity represents samples of the underlying distribution, or 2) they represent a parametric representation of a variational approximation of the posterior. We show that these approaches can be combined for an inference scheme that retains the advantages of both: it is able to represent multiple modes and arbitrary correlations, a feature of sampling methods, and it reduces the represented space to regions of high probability mass, a strength of variational approximations. neurally, the combined method can be interpreted as a feed-forward preselection of the relevant state space, followed by a neural dynamics implementation of Markov Chain Monte Carlo (MCMC) to approximate the posterior over the relevant states. We demonstrate the effectiveness and efficiency of this approach on a sparse coding model. In numerical experiments on artificial data and image patches, we compare the performance of the algorithms to that of exact EM, variational state space selection alone, MCMC alone, and the combined select and sample approach. The select and sample approach integrates the advantages of the sampling and variational approximations, and forms a robust, neurally plausible, and very efficient model of processing and learning in cortical networks. For sparse coding we show applications easily exceeding a thousand observed and a thousand hidden dimensions.

关键词： Neurons

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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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6th International conference on Bio-Inspired Computing: Theories and applications, BIC-TA 2011

作者： Xu, Jinbang Wu, Zhizhuo Yang, Xuan Ye, Jie Shen, Anwen Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Technology Huazhong University of Science and Technology 430074 Wuhan China

ISBN: (纸本)9780769545141

With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWMVSC) 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. © 2011 IEEE.

关键词： neural networks

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Quantitative Study and Monitoring of the Growth of Lung Cancer Nodule Using an X-Ray Computed Tomography image processing Tool

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11th International Work-conference on artificial neural networks (IWANN)

作者： Garcia Arroyo, Jose Luis Garcia Zapirain, Begona Mendez Zorrilla, Amaia Univ Deusto Deusto Inst Technol Deustotech Life Bilbao 48007 Spain

ISBN: (纸本)9783642214981

Nowadays there are millions of lung cancer patients around the world, and the number is increasing each year. In this context it is essential for medical radiologists and oncologists to properly control cancer evolution and to calculate quantitative values for its characterization. This paper presents a complete system integrating a software tool to improve the monitoring of patients' lung nodules and a complete stack of algorithms for a mathematical model to accurately calculate their growth/reduction. At the current moment we have a work in progress using a database with four patients who have been successfully tested, and whose nodules have all experienced positive growth, with an average of 31.72% in area growth and 0.28% per day in area growth speed. In the future, the database is expected to be enlarged with more patients so that numerical data can be obtained for use in statistical studies and mathematical modeling.

关键词： image Segmentation Otsu's method Lung cancer evolution Nodule growth Patient monitoring

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Filter Bank Common Spatial Pattern (FBCSP) algorithm using online adaptive and semi-supervised learning

Filter Bank Common Spatial Pattern (FBCSP) algorithm using o...

引用

International Joint conference on neural networks (IJCNN)

作者： Ang, Kai Keng Chin, Zheng Yang Zhang, Haihong Guan, Cuntai ASTAR Inst Infocomm Res Singapore 138632 Singapore

ISBN: (纸本)9781424496365

The Filter Bank Common Spatial Pattern (FBCSP) algorithm employs multiple spatial filters to automatically select key temporal-spatial discriminative EEG characteristics and the Naive Bayesian Parzen Window (NBPW) classifier using offline learning in EEG-based Brain-Computer Interfaces (BCI). However, it has yet to address the non-stationarity inherent in the EEG between the initial calibration session and subsequent online sessions. This paper presents the FBCSP that employs the NBPW classifier using online adaptive learning that augments the training data with available labeled data during online sessions. However, employing semi-supervised learning that simply augments the training data with available data using predicted labels can be detrimental to the classification accuracy. Hence, this paper presents the FBCSP using online semi-supervised learning that augments the training data with available data that matches the probabilistic model captured by the NBPW classifier using predicted labels. The performances of FBCSP using online adaptive and semi-supervised learning are evaluated on the BCI Competition IV datasets iia and iib and compared to the FBCSP using offline learning. The results showed that the FBCSP using online semi-supervised learning yielded relatively better session-to-session classification results compared against the FBCSP using offline learning. The FBCSP using online adaptive learning on true labels yielded the best results in both datasets, but the FBCSP using online semi-supervised learning on predicted labels is more practical in BCI applications where the true labels are not available.

关键词： BCI competition IV datasets Band pass filters Brain computer interfaces Calibration Electroencephalography Equations FBCSP NBPW classifier NaiÌve Bayesian Parzen Window classifier Signal processing algorithms Training data belief networks brain-computer interfaces channel bank filters classification accuracy electroencephalography filter bank common spatial pattern algorithm image classification key temporal spatial discriminative EEG characteristics learning (artificial intelligence) online adaptive learning online semisupervised learning probabilistic model probability session-to-session classification spatial filters training data

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Comparative algorithms for automatic detection of oil spill in multisar of RADARSAT-1 SAR and ENVISAT data

Comparative algorithms for automatic detection of oil spill ...

引用

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

作者： Maged Marghany Mazlan Hashim Institute of Geospatial Science and Technology (INSTeG) UniversitiTeknologi Malaysia Skudai Johor Malaysia

This study presents a comparative algorithms for oil spill automatic detection from different RADARSAT-1 SAR different mode data and ENVISAT ASAR data. Three algorithms are involved: Entropy, Mahalanobis, and artificial neural Network (ANN) algorithms. The study shows that ANN provide automatically oil spill detection with error of standard deviation of 0.12 which is lower than Entropy and the Mahalanobis algorithms.

关键词： Synthetic aperture radar artificial neural networks Entropy Classification algorithms Sea measurements conferences Training

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Informatics Engineering and Information Science, Part ii 2011

引用

丛书名： Communications in Computer and Information Science

2011年

作者： Azizah Abd Manaf Akram Zeki Mazdak Zamani Suriayati Chuprat Eyas El-Qawasmeh

ISBN: (数字)9783642254536

ISBN: (纸本)9783642254529

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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image texture classification using artificial neural Network (ANN)

Image texture classification using Artificial Neural Network...

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National conference on Emerging Trends and applications in Computer Science (NCETACS)

作者： Shohel Ali Ahmed Snigdha Dey Kandarpa Kumar Sarma Department of Electronics & Communication Technology Gauhati University Guwahati India

Texture classification is an important and challenging factor in image processing system which refers to the process of partitioning a digital image into multiple constituent segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. artificial neural Network (ANN) Based texture classification or Segmentation is an advanced technique providing rich information of an image of interest. As a part the work, an ANN is implemented to segment the image. For that a particular type of ANN is configured and trained so that it becomes capable of segmenting an image. The current work deals with a task where an object of interest is to be segmented out of a background for processes which can be carried out as part of extended applications.

关键词： artificial neural networks image segmentation Training Classification algorithms Pixel Feature extraction Neurons

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Rapid training data generation from image sequences for pattern recognition

Rapid training data generation from image sequences for patt...

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International conference on Mechatronics (ICM)

作者： Rasim Askin Dilan Ahmet Bugra Koku Erhan Ilhan Konukseven Department of Mechanical Engineering Middle East Technical University Ankara Turkey

This study focuses on the development of a novel technique for the rapid generation of artificial neural network training data from video streams. Videos captured on an off-road terrain are used to train artificial neural networks that learn to differentiate road and non-road sections in the captured videos. Contrary to the times-taking frame-by-frame processing, in the proposed method, classification data of road pixels is created concurrently as the video plays. The proposed method is explained in detail and its performance is evaluated against the classical hand-classified image sequences on test videos. The proposed method can also be applied to several other applications using training for recognition.

关键词： image segmentation artificial neural networks Roads Green products

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Towards real-time hyperspectral image processing, a GP-GPU implementation of target identification

Towards real-time hyperspectral image processing, a GP-GPU i...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： D. B. Heras F. Argüello J. López Gómez J. A. Becerra Richard J. Duro Grupo de Arquitectura de Computadores Universidade de Santiago de Compostela Spain Grupo Integrado de Ingeniería Universidade da Coruña Spain

In the quest for real time processing of hyperspectral images, this paper presents two artificial intelligence algorithms for target detection specially developed for their implementation over GPU and applied to a search-and-rescue scenario. Both algorithms are based on the application of artificial neural networks to the hyperspectral data. In the first algorithm the neural networks are applied at the level of individual pixels of the image. The second algorithm is a multiresolution based approach to scale invariant target identification using a hierarchical artificial neural network architecture. We have studied the main issues for the efficient implementation of the algorithms in GPU: the exploitation of thousands of threads that are available in this architecture and the adequate use of bandwidth of the device. The tests we have performed show both the effectiveness of detection of the algorithms and the efficiency of the GPU implementation in terms of execution times.

关键词： Graphics processing unit Object detection Hyperspectral imaging Instruction sets Parallel processing Neurons artificial neural networks

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Max-pooling convolutional neural networks for vision-based hand gesture recognition

Max-pooling convolutional neural networks for vision-based h...

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

作者： Jawad Nagi Frederick Ducatelle Gianni A. Di Caro Dan Cireşan Ueli Meier Alessandro Giusti Farrukh Nagi Jürgen Schmidhuber Luca Maria Gambardella Dalle Molle Institute for Artificial Intelligence (IDSIA) Dalle Molle Institute for Artificial Intelligence (IDSIA) University of Lugano Manno Lugano Switzerland Department of Mechanical Engineering University Tenaga National Putrajaya Selangor Darul Ehsan Malaysia

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance.

关键词： image color analysis Gesture recognition Training Convolution Real time systems Mobile robots

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