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共 276 条记录，以下是151-160 订阅

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Audio Environment Classication for Hearing Aids using Artificial Neural Networks with Windowed Input

Audio Environment Classication for Hearing Aids using Artifi...

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： C. Freeman R.D. Dony S.M. Areibi School of Engineering University of Guelph Guelph ONT Canada

Excessive background noise is one of the most common complaints from hearing aid users. Background noise classification systems can be used in hearing aids to adjust the response based on the noise environment. This paper examines and compares two promising classification techniques, non-windowed artificial neural networks (ANN) and hidden Markov models (HMM), with an artificial neural network using windowed input. Results obtained show that an ANN with a windowed input gives an accuracy of up to 97.9%, which is more accurate than both the non-windowed ANN and the HMM. Overall, a windowed ANN is able to give excellent accuracy and reliability and is considered to be a good model for background noise classification in hearing aids

关键词： Hearing aids Artificial neural networks Hidden Markov models Background noise Frequency Working environment noise Auditory system Acoustic noise computational intelligence signal processing

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Video Adaptation based on Affective Content with MPEG-21 DIA Framework

Video Adaptation based on Affective Content with MPEG-21 DIA...

引用

ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Min Xu Suhuai Luo Jesse Jin School of Design Communication and Information Technology University of Newcastle Callaghan NSW Australia

We present a video adaptation system which takes account of users' preference on video affective content (AC) and limited network resource. AC directly causes user's attention, evaluation and memory, which also provides feasible entry for video highlight. According to user's preference, the proposed adaptation insures the video parcels with AC are allocated as much as possible network resource. The system is implemented with MPEG-21 digital item adaptation (DIA) framework which provides a generic video adaptation solution for all video formats and various usage environments by manipulating on XML files. XML file based adaptation avoids complex video computation. 30 students from various departments were invited to test the system and their responses were positive

关键词： XML Engines Multimedia systems Motion pictures computational intelligence Video signal processing Computer architecture MPEG 7 Standard AC generators Bandwidth

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3D Volume Extraction of Densely Packed Cells in EM Data Stack by Forward and Backward Graph Cuts

3D Volume Extraction of Densely Packed Cells in EM Data Stac...

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ieee symposium on computational intelligence for Multimedia signal and Vision processing

作者： Yang, Huei-Fang Choe, Yoonsuck Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

ISBN: (纸本)9781424427710

3D reconstruction on dense nanoscale medical images is a very challenging research topic. The challenge comes from the fact that boundaries of objects on such images are not always very clear due to imperfect staining. This makes the segmentation of dense nanoscale medical images very difficult and thus increases the difficulty in 3D reconstruction. In this paper, we proposed a method based on watershed and an interactive segmentation technique, graph cuts, to extract 3D volumes from dense nanoscale medical images. In our method, images are first segmented by a marker-controlled watershed algorithm. Markers for watershed segmentation algorithm are seed points generated by using distance transform, followed by a new grouping method that clusters seed points that are too close. Regions obtained by watershed transform segmentation algorithms are considered as nodes in a graph. Edges are to connect between the nodes in adjacent image slices. The weight on each edge is defined based on the overlapped area between nodes. User-selected nodes (regions) in an initial image slice serve as hard constraints in the minimization process. A globally optimal 3D volume is obtained by minimizing MAP-MRF energy function via graph cuts. In our application, in order to obtain a complete 3D volume structures including branching, the final 3D volume is the union of two 3D volumes obtained by performing the minimization of MAP-MRF energy function using graph cuts forwards and backwards through the image stack. Experiments are conducted both on synthetic data and on nanoscale image sequences from the Serial Block Face Scanning Electron Microscope (SBF-SEM). The results show that our method can successfully extract 3D volumes.

关键词： image segmentation

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Toward under-specified queries enhancement using retrieval and classification platforms

Toward under-specified queries enhancement using retrieval a...

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Mustapha, Aouache Hussain, Aini Samad, Salina Abdul Zulkifley, Mohd Asyraf Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor De Malaysia

ISBN: (纸本)9781479945030

Radiography images are used usually for diseases detection and fracture that can be visible on lateral view. Poor contrast of x-ray images do not provide momentous information concerning pathologies that are of interest to the radiologist. Magnification of contrast and sharpness of x-ray images will afford plenty and satisfactory visual information to radiologist and clinician and thus, allow better segmentation and indexing subsequent modules in the computer aided diagnosis (CAD) system for an autonomous disease diagnosis. Therefore, in this paper it intends to describe a new strategy to cater for under-specified queries enhancement using retrieval and classification platforms. In the retrieval platform (RPF), gamma correction (GC) function was employed on the under-specified query (USQ) image to generate dispersion versus location (DL) descriptor that measures the relationship between the local contrast and the local brightness, measured respectively with the help of estimators of location and dispersion. Subsequently, it employs appropriate near optimal search between the DL features of the USQ image and the corresponding similarity measurement in the archive database. In the classification platform (CPF), an approach was examined to predict the gain value of GC function using statistical pixel-level (SPL) features extracted from the radiography images along with the ANNs model classifier. The quality of the retrieved image is determined by referring to the USQ image. In addition, the problem of gain value estimation is transformed to a classification problem solved using an ANN model with three different measurement modes. Results indicated that the proposed approach can significantly improved image quality as confirmed by the DL descriptor which shown a more balance condition.

关键词： diagnostic radiography feature extraction image classification image enhancement image retrieval image segmentation medical image processing neural nets patient diagnosis ANN model CAD system CPF DL descriptor GC function RPF SPL features extraction USQ Xray image autonomous disease diagnosis computer aided diagnosis dispersion versus location descriptor gamma correction image classification platform image retrieval platform radiography image statistical pixel-level under-specified query image enhancement Databases Feature extraction image quality Medical diagnostic imaging Predictive models Visualization

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AdaBoost in basketball player identification

AdaBoost in basketball player identification

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13th ieee International symposium on computational intelligence and Informatics (CINTI)

作者： Ivankovic, Z. Markoski, B. Ivkovic, M. Radosav, D. Pecev, P. Univ Novi Sad Tech Fac Mihajlo Pupin Zrenjanin Serbia Univ Novi Sad Fac Sci Novi Sad Serbia

ISBN: (纸本)9781467352062;9781467352055

Video materials contain huge amount of information. Their storage in databases and analysis by various algorithms represents an area that constantly develops. This paper presents the process of analysis of basketball games by AdaBoost algorithm. This algorithm is mainly used for face recognition and body parts recognition. It consists of a linear combination of weak classifiers. In this paper, stumps were used as weak classifiers. The aim of this research is to assess the accuracy of this algorithm when applied in players' identification at basketball games. Capabilities of AdaBoost were examined when applied to video footage from single moving camera, and when these footages were not previously treated by any other algorithm. The first training was performed by entire images of basketball players, whereas the second training was performed by using the images of the head and torso. By applying the algorithm to the given set of images that include head and torso, the algorithm obtained an accuracy of 70.5%. Experimental results have shown that training on the set of entire body images was not possible due to large amount of background that goes into the training, and which represents noise in training process. This accuracy could be increased by applying filters that would remove background from images and leave just basketball players. By applying those filters, the amount of noise in the training data would be significantly reduced.

关键词： cameras face recognition image classification learning (artificial intelligence) sport video signal processing

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Performance of Overloaded MIMO-OFDM System with Repetition Code

Performance of Overloaded MIMO-OFDM System with Repetition C...

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International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Matsuoka, Hikari Doi, Yoshihito Yabe, Tatsuro Sanada, Yukitoshi Keio Univ Dept Elect & Elect Engn Yokohama Kanagawa Japan

ISBN: (纸本)9781479961207

This paper investigates the performance of an overloaded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with a repetition code. It has been demonstrated that diversity with block coding prevents the performance degradation induced by signal multiplexing. However, the computational complexity of a joint decoding scheme increases exponentially with the number of multiplexed signal streams. Thus, this paper proposes the use of a repetition code in the overloaded MIMO-OFDM system. In addition, QR decomposition with M-algorithm (QRM) maximum likelihood decoding (MLD) is applied to the decoding of the repetition code. QRM-MLD significantly reduces the amount of joint decoding complexity. In addition, virtual antennas are employed in order to increase the throughput that is reduced by the repetition code. It is shown that the proposed scheme reduces the complexity by about 1/48 for 6 signal streams with QPSK modulation while the BER degradation is less than 0.1dB at the BER of 10(-3).

关键词： MIMO communication OFDM modulation error statistics maximum likelihood decoding BER degradation QR decomposition with M-algorithm QRM maximum likelihood decoding block coding computational complexity joint decoding scheme multiple-input multiple-output multiplexed signal streams orthogonal frequency division multiplexing overloaded MIMO-OFDM system repetition code signal multiplexing virtual antennas computational modeling Decoding Encoding Joints Multiplexing Throughput Transmitting antennas repetition coding Orthogonal frequency division multiplexing Block codes multiple input multiple output systems maximum likelihood decoding MULTIPLEXOR DECODING transmitting antennas MIMO communication Throughput decoding scheme computational modeling Joints Joints complexity classes

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Identification of Dynamic Nonlinear Systems using computational intelligence Techniques

Identification of Dynamic Nonlinear Systems using Computatio...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Claudio Turchetti Francesco Gianfelici Dipartimento di Elettronica Intelligenza Artificiale e Telecomunicazioni (DEIT) Università Politecnica delle Marche Ancona Italy

A novel approach based on computational intelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loeve transform in representing stochastic processes and the approximation capabilities of multi-layer neural networks. Experimental results on nonlinear systems governed by difference equations demonstrate the effectiveness of the proposed approach that is based on a real-time learning algorithm. Exhaustive experimentation on specific case studies was performed and some experimental results were compared with other existing techniques such as the Lee-Schetzen method, least mean square (LMS), recursive least square (RLS) and normalized least mean square (NLMS) algorithms. A better identification-accuracy was also achieved, and a reduction of some orders of magnitude in training-times compared with the well-known Lee-Schetzen method was obtained, thus making the proposed methodology one of the current best practices in this field

关键词： Nonlinear systems computational intelligence Least squares approximation Karhunen-Loeve transforms Stochastic processes Multi-layer neural network Difference equations Real time systems Least squares methods Resonance light scattering

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Function Approximation Through Growing Neural Network Based On RBF And Potential Functions

Function Approximation Through Growing Neural Network Based ...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Iren Valova Natacha Gueorguieva George Georgiev Computer Science University of Massachusetts Dartmouth North Dartmouth MA USA Computer Science City College and Graduate Center City University of New York Staten Island NY USA Computer Science University of Wisconsin Oshkosh Oshkosh WI USA

This research work proposes a neural network based on RBFs with symmetrical potential functions and two fundamental components - potential function generators (PFG) and potential function entities (PFE). The approach, based on RBFNs with symmetrical potential functions (SPF), performs a mapping based on a set of generated potential fields over the domain of input space by a number of potential function entities. The placement and parameterization of the local units as well as the choice of their number is difficult and critical part for RBF networks. Networks with too many parameters can overfit data and have poor generalization. The presented method allows effective determination of all these values automatically. The proposed approach is suitable for on-line and off-line applications

关键词： Function approximation Neural networks Computer science Neurons signal generators Multidimensional systems State estimation Kernel computational intelligence Multidimensional signal processing

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Selective Bayesian estimation for efficient super-resolution

Selective Bayesian estimation for efficient super-resolution

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Fourth ieee International symposium on signal processing and Information Technology, ISSPIT 2004

作者： Ivanovski, Zoran A. Karam, Lina J. Abousleman, Glen P. Department of Electrical Engineering Arizona State Univesity Tempe AZ 85287-5706 Compression Communications and Intelligence Lab. General Dynamics C4 Systems Scottsdale AZ 85257

ISBN: (纸本)0780386892

In this paper, a new approach to efficient and robust super-resolution is presented. Our method is based on selectively applying a Bayesian MAP estimator to image regions with high spatial activity. The degree of spatial activity is measured using the gradient of the estimated high-resolution image at each iteration. In addition, selective filtering is applied to enhance the visual quality of the estimated high-resolution image. The results obtained via simulation and with real video sequences demonstrate up to a 50% reduction in computational complexity, with improved visual quality, and higher SNR gains for magnification factors of four or more. © 2004 ieee.

关键词： image analysis

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Experience Based Surface Discernment by a Quadruped Robot

Experience Based Surface Discernment by a Quadruped Robot

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Lars Holmstrom Andrew Toland George Lendaris NW Computational Intelligence Laboratory Portland State University OR USA

The task of autonomous surface discernment by an AIBO robotic dog is addressed. Different surface textures (plywood board, thin foam, short carpet, shag carpet) as well as different inclines (0 and 10 degrees) are considered. Using a genetic algorithm, gaits are designed which allow the robot to traverse each of these surfaces in an (approximately) optimal fashion. Frequency domain analysis of actuator readings from individual leg joints is performed for data collected using each gait on each surface type. It is found that the spectral content of these signals is significantly dependent on the characteristics of both the gait in use and the surface being walked upon. Using tap-delay Adaline neural networks to integrate actuator readings from 15 independent joints into a set of models of different gait/surface experiences, an algorithm is designed which uses these experiences to yield high classification rates across surface transitions and with low latency

关键词： Legged locomotion Mobile robots computational intelligence Intelligent robots Genetic algorithms Algorithm design and analysis Actuators Leg Design engineering Friction

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