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

共 2664 条记录，以下是1911-1920 订阅

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Nonlinear dimension reduction using ISOMap based on class information

Nonlinear dimension reduction using ISOMap based on class in...

引用

International Joint conference on neural networks (IJCNN)

作者： Minkook Cho Hyeyoung Park School of Electrical Engineering and Computer Science Kyungpook National University South Korea

image processing and machine learning communities have long addressed the problems involved in the analysis of large high-dimensional data sets. To deal with high-dimensional data efficiently, learning core properties of given data set is important. The manifold learning methods such as ISOMap try to identify a low-dimensional manifold from a set of unorganized samples. ISOMap method is an extension of the classical multidimensional scaling method for dimension reduction, which find a linear subspace in which dissimilarity between data points is preserved. In order to measure dissimilarity, ISOMap uses the geodesic distances on the manifold instead of Euclidean distance. In this paper, we propose a modification of ISOMap using class information, which is often given in company with input data in many applications such as pattern classification. Since the conventional ISOMap does not use class information in approximating true geodesic distance between each pair of data points, it is difficult to construct a data structure related to class-membership that may give important information for given task such as data visualization and classification. The proposed method utilizes class-membership for measuring distance of data pair so as to find a low-dimensional manifold preserving the distance between classes as well as the distance between data points. Through computational experiments on artificial data sets and real facial data sets, we confirm that the proposed method gives better performance than the conventional ISOMap.

关键词： Manifolds Level measurement image processing Machine learning image analysis Learning systems Multidimensional systems Euclidean distance Pattern classification Data structures

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Intelligent Predictive Control - Application to Scheduled Crystallization Processes

Intelligent Predictive Control - Application to Scheduled Cr...

引用

International conference on Adaptive and Intelligent Systems, ICAIS

作者： Luis Alberto Paz Suarez Petia Georgieva Sebastião Feyo de Azevedo Department of Chemical Engineering University of Porto Portugal Signal Processing Lab University of Aveiro Portugal

The purpose of this paper is twofold. On one hand, we propose a modification of the general Model Predictive Control (MPC) approach where a prespecified tracking error is tolerated. The introduction of error tolerance (ET) in the MPC optimization algorithm reduces considerably the average duration of each optimization step and makes the MPC computationally more efficient and attractive for industrial applications. On the other hand a challenging scheduled crystallization process serves as a case study to show the practical relevance of the new intelligent predictive control. Comparative tests with different control policies are performed: (i) Classical MPC with analytical or artificial neural Network (ANN) process model; (ii) ET MPC with analytical or ANN process model; (iii) Proportional-Integral (PI) control. Besides the computational benefits of ET MPC, the integration of ANN into the ET MPC brings substantial improvements of the final process performance measures and further relaxes the computational demands.

关键词： Intelligent control Predictive control Crystallization Magnetic materials Photonic crystals artificial neural networks Job shop scheduling Pi control Proportional control Performance analysis

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A novel SoC architecture on FPGA for ultra fast face detection

A novel SoC architecture on FPGA for ultra fast face detecti...

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IEEE International conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Chun He Alexandros Papakonstantinou Deming Chen Research Institute of Electronic Science & Tech University of Electronic Science and Technology China Electrical & Computer Engineering University of Illinois Urbana-Champaign IL USA

Face detection is the cornerstone of a wide range of applications such as video surveillance, robotic vision and biometric authentication. One of the biggest challenges in face detection based applications is the speed at which faces can be accurately detected. In this paper, we present a novel SoC (System on Chip) architecture for ultra fast face detection in video or other image rich content. Our implementation is based on an efficient and robust algorithm that uses a cascade of artificial neural Network (ANN) classifiers on AdaBoost trained Haar features. The face detector architecture extracts the coarse grained parallelism by efficiently overlapping different computation phases while taking advantage of the finegrained parallelism at the module level. We provide details on the parallelism extraction achieved by our architecture and show experimental results that portray the efficiency of our face detection implementation. For the implementation and evaluation of our architecture we used the Xilinx FX130T Virtex5 FPGA device on the ML510 development board. Our performance evaluations indicate that a speedup of around 100X can be achieved over a SSE-optimized software implementation running on a 2.4 GHz Core-2 Quad CPU. The detection speed reaches 625 frames per sec (fps).

关键词： Field programmable gate arrays Face detection Computer architecture Parallel processing artificial neural networks Video surveillance Robot vision systems Biometrics Authentication System-on-a-chip

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Application of digital image processing method for fish age estimation

Application of digital image processing method for fish age ...

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conference on applications of Digital image processing XXXI

作者： Wu Ting-wan Hong Jian-hua S China Univ Technol Sch Phys Sci & Technol Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9780819472939

In this paper, the study results on the fish age estimation issues, as an application of the method of digital image processing, presented by analysis from otolith image. First, a kind of artificial neural Network (ANN), the Pulse-coupled neural networks (PCNN), is proposed, and used to identify the different summer or winter year-rings patterns. Second, a well-founded approach, using morphological features, is brought forwand to automatically detect the nucleus within the otolith images. Finally, the Morphological Method is used to deduce the fish's age. The results of this paper maybe significant in fishery research, and the methods can be used in other biologic features identify fields.

关键词： digital image processing fish age estimation morphological detection artificial neural networks patterns classification

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Recognition of selected fingerprints and iris features enhanced by curvelet transform with artificial neural networks

Recognition of selected fingerprints and iris features enhan...

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15th International conference on Systems, Signals and image processing

作者： Altun, Adem Alpaslan Selcuk Univ Dept Elect & Comp Educ Tech Educ Fac TR-42075 Konya Turkey

ISBN: (纸本)9788022728560

Biometric systems based on one-modal biometrics are often not able to meet the desired performance requirements for large user population applications, due to problems such as noisy data, intra-class variations, restricted degrees of freedom, non-university, spoof attacks, and unacceptable error rates. Therefore, multimodal biometrics refers to the use of a combination of two or more biometric modalities in a single recognition or identification system. In order to ensure that the performance of multibiometric systems such as fingerprint and iris will be powerful with respect to the quality of obtained fingerprint and iris images, these images are denoised and enhanced. In this study, curvelet transform is applied biometric images for enhancement. Obtained results after applied curvelet transform is compared to the other traditional image enhancement algorithms. Features obtained from enhanced fingerprints and iris images are selected by using Genetic Algorithms because of too huge dataset. Selected features are input to artificial neural networks for biometric recognition. Thus, the recognition is achieved very fast without to reduce the performance.

关键词： multibiometric recognition image enhancement image denoising artificial neural networks

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Wavelets Pre-processing of artificial neural networks Classifiers

Wavelets Pre-Processing of Artificial Neural Networks Classi...

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5th International Multi-conference on Systems, Signals and Devices

作者： Al-Haj, Ali Princess Sumaya Univ Technol King Abdullah II Sch Elect Engn Dept Comp Engn Amman 11941 Jordan

ISBN: (纸本)9781424422050

artificial neural networks are highly parallel structures inspired by the human brain. They have been used successfully in many human-like applications, such as pattern recognition. Performance of these networks can be enhanced if used properly in conjunction with equally powerful mathematical tools. In this paper, we used the discrete wavelet transform as a pre-processing tool for two well-known neural classifiers;Competitive Layer networks and Learning Vector networks. The wavelets transform was used successfully to approximate the input patterns of the two classifiers and thus reduced their input-layer requirements considerably. Such reduction facilitates cost-effective hardware implementations of artificial neural networks.

关键词： Discrete wavelet transforms

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Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Multidimensional polynomial powers of sigmoid (PPS) Wavelet ...

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1st International conference on Bio-Inspired Systems and Signal processing

作者： Marar, Joao Fernando Coelho, Helder Sao Paulo State Univ Fac Ciencias Dept Comp Adapt Syst & Computat Intelligence Lab Sao Paulo Brazil

ISBN: (纸本)9789898111180

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

关键词： artificial neural network function approximation polynomial powers of sigmoid (PPS) wavelets functions PPS-Wavelet neural networks activation functions feedforward networks

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Fast object tracking through the use of artificial neural networks

Fast object tracking through the use of artificial neural ne...

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IASTED International conference on artificial Intelligence and applications, AIA 2008

作者： Smith, David T. Computer Science Department Indiana University of Pennsylvania Indiana Pa. United States

ISBN: (纸本)9780889867093

A key function for an autonomous robot is recognition and tracking of pertinent objects observed through a camera. Real-time interpretation of camera images is critical to a robot's interaction with the physical world. This paper presents preliminary results in using artificial neural networks (ANN) to examine the pixels of an image. While processing all pixels through ANNs would jeopardize the real-time processing requirements, the accuracy gained facilitates use of algorithms that only need to examine a fraction of the pixels composing an image in order to recognize and track the objects of interest. Presented herein is a test environment and problem statement, description of a methodology that employed ANNs to address the problem, and details of the algorithms that interpret images. The results show a high degree of success within the domain of the test environment.

关键词： neural networks

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image Clustering with Spiking Neuron Network

Image Clustering with Spiking Neuron Network

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International Joint conference on neural networks

作者： Meftah, B. Benyettou, A. Lezoray, O. QingXiang, W. Ctr Univ Mustapha Stambouli Equipe EDTEC LRSBG Mascara Algeria Univ Mohamed Boudiaf Lab Signal Image & Parole SIMPA Oran Algeria Univ Caen CNRS GREYC UMR 6072 F-14050 Caen France Univ Ulster Magee Derry Sch Comp & Intelligent Syst Londonderry BT48 7JL North Ireland

ISBN: (纸本)9781424418206

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron networks (SNNs) are often referred to as the 3(rd) generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation.

关键词： neural networks

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Research on the application of biomimetic computing in speech recognition

Research on the application of biomimetic computing in speec...

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International conference on Audio, Language and image processing

作者： Guo, Chunyi Li, Runzhi Shi, Lei Zhengzhou Univ Sch Informat Engn Zhengzhou Henan Peoples R China

ISBN: (纸本)9781424417230

Since speech is one of the most direct and effective means of human communication, it's natural to apply biomimetic processing mechanism to automatic speech recognition to solve the existing speech recognition problems. In this paper, three typical techniques were selected respectively: simulated evolutionary computation(SEQ, artificial neural network(ANN) and fuzzy logic and reasoning technique, from intelligence building processing simulation, intelligence structure simulation and intelligence behavior simulation, to identify their applications in different stages of speech recognition. A SEC usage and the corresponding algorithm were presented to generate training speech data, and a method of Elman network as acoustic model was also presented.

关键词： Speech recognition Speech artificial neural networks Hidden Markov models Strontium Biological system modeling Training

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