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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1731-1740 订阅

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Reflections on neural networks as Repetitive Structures with Several Equilibria and Stable Behavior

Reflections on Neural Networks as Repetitive Structures with...

引用

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

作者： Rasvan, Vladimir Univ Craiova Dept Automat Control Craiova 200585 Romania

ISBN: (纸本)9783642386817

The structures of the artificial Intelligence (AI) are sometimes "created" in order to solve specific problems of science and engineering. They may be viewed as dedicated signal processors, with dedicated, in particular repetitive, structure. In this paper such structures of neural networks (NN)-like devices are considered, having as starting point the problems in Mathematical Physics. Both the ways followed by such inferences and their outcomes may be quite diverse one of the paper's aims is to illustrate this assertion. Next, ensuring global stability and convergence properties in the presence of several equilibria is a common feature of the field. The general discussion on the "emergence" of Al devices with NN structure is followed by the presentation of the elements of the global behavior for systems with several equilibria. The approach is illustrated on the case of the M-lattice;in tackling this application there is pointed out the role of the high gain to ensure both gradient like behavior combined with binary outputs which are required e.g. in image processing.

关键词： image processing

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Acute Lymphoblastic Leukemia Identification Using Blood Smear images and a neural Classifier

Acute Lymphoblastic Leukemia Identification Using Blood Smea...

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

作者： Khashman, Adnan Abbas, Hayder Hassan Near East Univ ISRC TR-10 Mersin Turkey

ISBN: (纸本)9783642386817

There is a need for fast and cost-effective leukemia identification methods, because early identification could increase the likelihood of recovery. Currently, diagnostic methods require sophisticated expensive laboratories such as immune-phenotype and cytogenetic abnormality. Therefore, we propose an identification method based on using blood smear images of normal and cancerous cells, in addition to a neural network classifier. We focus in this paper on identifying Acute Lumphoblastic Leukemia (ALL) cases, and implement our experiments following three learning schemes for a neural model. The neural classifiers distinguish between normal blood cells and ALL-infected cells. The experimental results show that the proposed novel leukemia identification system can be effectively used for such a task, and thus could be implemented for identifying other leukemia types in real life applications.

关键词： Acute lymphoblastic leukemia (ALL) Blood cell identification neural networks Medical image processing

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Point Cloud Data Filtering and Downsampling using Growing neural Gas

Point Cloud Data Filtering and Downsampling using Growing Ne...

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International Joint conference on neural networks (IJCNN)

作者： Orts-Escolano, Sergio Morell, Vicente Garcia-Rodriguez, Jose Cazorla, Miguel Univ Alicante Dept Comp Technol Alicante Spain Univ Alicante Dept Sci Computat & Artificial Intelligence Dept Alicante Spain

ISBN: (纸本)9781467361293;9781467361286

3D sensors provide 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 downsampling 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 GNG method yields better input space adaptation to noisy data than other filtering and downsampling methods like Voxel Grid. It is also demonstrated how the state-of-the-art keypoint detectors improve their performance using filtered data with GNG network. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

关键词： feature extraction image classification image registration image sensors mesh generation mobile robots neural nets object recognition robot vision

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Fully-Digital Oscillatory Associative Memories Enabled by Non-volatile Logic

Fully-Digital Oscillatory Associative Memories Enabled by No...

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International Joint conference on neural networks (IJCNN)

作者： Calayir, Vehbi Pileggi, Larry Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781467361293;9781467361286

Due to its brain-like parallel processing, neurocomputing has been regarded as intriguing alternative to traditional von Neumann architectures for such applications as image processing, pattern recognition, and associative memory. Associative memories based on neurocomputing attempt to mimic the human brain via a parallel network of coupled artificial neurons. Oscillatory neural networks (ONNs) have been proposed for such purposes;however, CMOS-based implementations would be inefficient due to the corresponding circuit complexity of oscillators and phase-locking mechanisms. In addition, programmability of the synaptic weights would require numerous reconfigurable, complex analog circuits that represent an impractical power and area overhead. In this paper we propose a fully-digital ONN architecture that is enabled by non-volatile logic. Using a newly proposed all-magnetic logic family, mLogic, we demonstrate the efficacy of digitizing the oscillators and phase relationships by exploiting the inherent storage. We perform a device-level simulation-based comparison of mLogic and 32nm CMOS for a fully-interconnected 60-neuron system, and show approximately 15x area improvement and 18x power improvement that would be achieved for a large system with 100k neurons.

关键词： CMOS memory circuits circuit oscillations circuit simulation content-addressable storage memory architecture neural chips

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Pattern Recognition in Mammographic images Used by the Residents in Mammography

Pattern Recognition in Mammographic Images Used by the Resid...

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International conference on Computer Medical applications (ICCMA)

作者： dos Santos, Jamilson Bispo de Almeida Junior, Jorge Rady Silva, Leandro Augusto Univ Sao Paulo Escola Politecn Dept Engn Comp & Sistemas Digitais BR-09500900 Sao Paulo Brazil

ISBN: (纸本)9781467352147;9781467352130

This paper presents a computational strategy for content based image retrieval (CBIR-Content-Based image Retrieval), considering the similarity in relation to an image already selected. The identification of similarity is obtained by feature extraction, using the technique of wavelet combined with Hu moments. The classification of mammographic is performed using artificial neural networks, through the classifier Self-Organizing Map (SOM). The proposed method is tested with a database of the Laboratory of Medical image Classification (QUALIM) Department of Diagnostic Imaging, Federal University of Sao Paulo (UNIFESP).

关键词： Data Mining Digital image processing Pattern Recognition CBIR SOM

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image data processing via neural networks for tool wear prediction

Image data processing via neural networks for tool wear pred...

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8th CIRP International conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME)

作者： D'Addona, D. M. Teti, R. Univ Naples Federico II Dept Mat & Prod Engn Fraunhofer Joint Lab Excellence Adv Prod Technol Piazzale Tecchio 80 I-80125 Naples Italy

In the manufacturing systems, one of the most important issues is to estimate the rest of cutting tool life under a given cutting conditions as accurately as possible. In fact, machining efficiency is easily influenced by the kind of tool selected at each cutting process. One of the most complex problems for tool selection is that of estimating the tool life under a given cutting conditions as accurately as possible. As the quality of the cutting tool is directed related to the quality of product, the level of tool wear should be kept under control during machining operations. In order to monitor the tool wear development during machining processes, the interface chosen between the working procedure and the computer was a digital image of the cutting tool detected by an optical sensor. images, however, are not homogeneous. images with standard size and pixel density were produced elaborating tool images files obtained during machining tests. This paper is focused on a procedure for the processing of cutting tool images detected during tests. A methodology to design and optimized artificial neural networks for automatic tool wear recognition using standard images of cutting tool is proposed. (C) 2013 The Authors. Published by Elsevier B.V.

关键词： Cutting tool wear image processing neural networks

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Estimation of the RiiG-Distribution Parameters Using the artificial neural networks

Estimation of the RiIG-Distribution Parameters Using the Art...

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3rd IEEE International conference on Signal and image processing applications (ICSIPA)

作者： Mezache, Amar Chalabi, Izzeddine Univ Constantine 1 Lab Signaux & Syst Commun Dept Elect Constantine 25010 Algeria Univ Msila Fac Technol Dept Elect Msila 28000 Algeria

ISBN: (纸本)9781479902699;9781479902675

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： Gaussian distribution Gaussian processes genetic algorithms inverse problems iterative methods mathematics computing mean square error methods neural nets parameter estimation regression analysis

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Sensor Assisted Motion Estimation

Sensor Assisted Motion Estimation

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Students conference on Engineering and Systems (SCES) - Inspiring Engineering and Systems for Sustainable Development

作者： Areekath, Lakshmi Palavalasa, Kranthi Kumar Vellore Inst Technol Sensor Syst Technol Vellore 632014 Tamil Nadu India

ISBN: (纸本)9781467356305;9781467356282

Linear displacement estimation of a mobile device is useful in various real time applications, such as Global Motion Estimation (GME) in video encoding, video surveillance etc. Present state of the art techniques perform the global motion estimation using techniques predominantly based on image processing algorithms. These require complex computations and more power consumption. In this paper a novel approach, Sensor assisted Motion Estimation (SaME), to estimate the linear displacement of a mobile device using inbuilt sensors, is proposed. SaME uses inbuilt 3-axis accelerometer to determine the linear displacement along X, Y and Z axes. SaME algorithm is a combination of basic image processing techniques, sensor data and artificial Intelligence. The artificial neural Network (ANN) is used to build the artificial intelligence. ANN is trained to map the sensor data from inbuilt accelerometer to the corresponding displacement data acquired via simple image processing techniques. The resultant model is used to estimate the linear displacement of the mobile device using the sensor data as input. From the results it can be concluded that the inbuilt sensors can be used to arrive at reliable estimates of linear displacement via the proposed method.

关键词： Accelerometer Global motion estimation (GME) neural networks Sensors Video Encoding

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Roughness evaluation of vine leaf by image processing

Roughness evaluation of vine leaf by image processing

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IASTED International conference on Signal processing, Pattern Recognition and applications, SPPRA 2013

作者： Bediaf, Houda Journaux, Ludovic Sabre, Rachid Cointault, Frédéric AgroSup Dijon 26 Bd. Docteur Petitjean 21079 Dijon Cedex France

The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary products and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy-drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The development and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The system for image acquisition of leaf surface consists of scanning electron microscope (SEM). The images of leaf surface are captured and analyzed to estimate the optical roughness. 2-D Fast Fourier Transform (FFT) algorithm and Co-occurrence Matrix are used for texture analysis. A multilayer perceptron (MLP) neural network is used to model and predict the optical roughness values.

关键词： Computer vision

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Displacement Prediction for Soil Nailing Based on ANN

Displacement Prediction for Soil Nailing Based on ANN

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3rd International conference on Civil Engineering, Architecture and Building Materials (CEABM 2013)

作者： Gao, Lang Tang, Zhaowen Liu, Quanzhong Zhejiang Zhongshe Engn Design Co Ltd Hangzhou Hangzhou Zhejiang Peoples R China Zhejiang Urban & Rural Plan Des Inst Hangzhou Peoples R China Hangzhou Architectur Des Inst Hangzhou Peoples R China

ISBN: (纸本)9783037857755

Soil nailing has become an important excavation support system for its good performance and cost-effectiveness. It is complicated to predict deformation of soil nailing during excavating. The artificial neural Network (ANN) is developed very quickly these years, which can be applied in diverse applications such as complex non-linear function mapping, pattern recognition, image processing and so on, and has been widely used in many fields, including geotechnical engineering. In this paper, the artificial neural network is applied for deformation prediction for soil nailing in deep excavation. The time series neural networks-based model for predicting deformation is presented and used in an engineering project. The results predicted by the model and those observed in the field are compared. It is shown that the artificial neural network-based method is effective in predicting the displacement of soil nailing during excavation.

关键词： Soil Nailing Displacement Prediction artificial neural Network

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