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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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An iterative fuzzy segmentation algorithm for recognizing an odor source in near shore ocean environments

An iterative fuzzy segmentation algorithm for recognizing an...

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International symposium on computational intelligence in Robotics and Automation

作者： Li, Wei Calif State Univ Bakersfield Dept Comp Sci Bakersfield CA 93311 USA

ISBN: (纸本)9781424407897

A mission of chemical plume tracing (CPT) in near-shore and ocean environments is to navigate an autonomous underwater vehicle (AUV) to find a chemical plume, to trace the plume to its source, and to declare the source location. It is necessary to recognize the declared odor source by using a visual system. Color images, which were taken in near-shore ocean environments when the source was declared, are very vague due to dim illumination conditions and fluid advection effects. This paper presents an iterative fuzzy segmentation (IFS) algorithm for extracting color components of the chemical plume and the odor source for visual confirmation of the correct declared odor source. The proposed approach might be of general interest in image processing and computer vision.

关键词： autonomous underwater vehicles chemical plume tracing fuzzy image segmentation odor source recognition

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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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Neural Networks Applied for Cork Tiles image Classification

Neural Networks Applied for Cork Tiles Image Classification

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

作者： A. Georgieva I. Jordanov T. Rafik School of Computing University of Portsmouth Portsmouth UK School of Product and Engineering Design University of Wales Institute of Science and Technology Cardiff UK

In this paper a hybrid global optimization method GLPTS is further investigated and applied for feed-forward neural networks supervised learning. The method is initially tested on several benchmark problems and subsequently employed for pattern recognition problem. The proposed technique is used for training neural networks (NN) that have to inspect and classify three types of cork tiles images. During the feature extraction phase, statistical textural characteristics are obtained from the tiles' images and then used for training several different NN architectures. Results from the testing phase are discussed and analysed, showing good generalization abilities of the trained NN. Finally, directions of future work are briefly stated

关键词： Neural networks Tiles image classification Optimization methods Feedforward systems Feedforward neural networks Supervised learning Benchmark testing Pattern recognition Feature extraction

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image Segmentation using a Weighted Kernel PCA Approach to Spectral Clustering

Image Segmentation using a Weighted Kernel PCA Approach to S...

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

作者： Carlos Alzate Johan A. K. Suykens Department of Electrical Engineering ESATSCD-SISTA Katholieke Universiteit Leuven Leuven Belgium

In classical graph-based image segmentation, a data-driven matrix is constructed representing similarities between every pair of pixels. The eigenvectors of such matrices contain relevant information about the clusters present on the image. An approach to image segmentation using spectral clustering with out-of-sample extensions is presented. This approach is based on the weighted kernel PCA framework. An advantage of the proposed method is the possibility to train and validate the clustering model on subsampled parts of the image to be segmented. The cluster indicators for the remaining pixels can then be inferred using the out-of-sample extension. This subsampling scheme can be used to reduce the computation time of the segmentation. Simulation results with grayscale and color images show improvements in terms of computation times together with visually appealing clusters

关键词： image segmentation Kernel Principal component analysis Pixel Clustering algorithms Optimization methods Matrix converters Matrix decomposition Partitioning algorithms Iterative algorithms

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A video coprocessor for mobile multi media signal processing

A video coprocessor for mobile multi media signal processing

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ieee International symposium on Consumer Electronics

作者： Stabernack, Benno Weis, Kai-Immo Huebert, Heiko Heinrich Hertz Inst Nachrichtentech Berlin GmbH Image Proc Dept D-10587 Berlin Germany

ISBN: (纸本)9781424411092

The new DVB-H standard allows broadcasting of audio/video content to mobile terminals. Such devices underlie severe restrictions concerning processing load and power consumption. The most computational intensive and therewith most power consuming part of such terminals is the decoding of the H.264/AVC video datastream. We present optimization strategies for software-based H.264/AVC video decoding as well as the architecture of an H.264/AVC decoding companion chip with specialized coprocessors, which targets the above mentioned restrictions(1).

关键词： DVB-H H.264/AVC decoder SOC

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Evolutionary Algorithms in the Classification of Mammograms

Evolutionary Algorithms in the Classification of Mammograms

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

作者： D. C. Hope E. Munday S. L. Smith Department of Electronics University of York York UK

The application of pattern recognition techniques to radiology has the potential to detect cancer earlier and save lives, and consequently much research has been devoted to this problem. This worked tackled a subset of the problem, investigating a novel method of classifying mammograms using an evolutionary approach known as Cartesian genetic programming (CGP). Microcalcifications, one of two major indicators of cancer on mammograms, were used for the classification. A large software framework was written in order to investigate this, which allows the viewing of images, manual segmentation of lesions and then automatic classification. Two classification approaches were pursued, the first classifying on texture features and the second, a new approach, classifying by using the lesion's raw pixel array. Early results using the system showed some potential. It was found that during training, networks could obtain correct classification rates of between 80 and 100%. The best results were approaching those in the contemporary literature and suggest the technique warrants further investigation

关键词： Evolutionary computation Breast cancer Cancer detection Pattern recognition image segmentation image processing computational intelligence signal processing algorithms Radiology Genetic programming

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Sequential Subspace Estimator for biometric authentication

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NEUROCOMPUTING 2015年 148卷 294-309页

作者： Malek, Obaidul Venetsanopoulos, Anastasios Androutsos, Dimitrios Zhao, Lian Ryerson Univ Toronto ON Canada

The principal challenge in biometric authentication is to mitigate the effects of any interference while extracting the biometric features for biometric template generation. Most biometric systems are developed under the assumption that extracted biometrics and the nature of their associated interferences are linear, stationary, and homogeneous. The performance of biometric authentication deteriorates when the underlying assumptions are violated due to nonlinear, nonstationary, and heterogeneous noise. Therefore, a more sophisticated filtering method needs to be developed to deal with these challenges. In this paper, a new Sequential Subspace Estimator (SSE) algorithm for biometric authentication is proposed. In the proposed method, a sequential estimator is being designed in the image subspace which addresses the challenges due to nonlinear, nonstationary, and heterogeneous noise. Furthermore, the proposed method includes a subspace technique that overcomes the computational complexity associated with the sequential estimator. The theoretical foundation of the proposed method along with the experimental results is also presented in this paper. For the experimental evaluation of the proposed method, we use facial images from two public databases: the "Put Face Database" and the "Indian Face Database". The experimental results demonstrate the superiority of the proposed method in comparison with its counterparts. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Biometric authentication image subspace Kalman filter PCA Sequential estimator

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Robust designs for templates of directional extraction cellular neural network with application

Robust designs for templates of directional extraction cellu...

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International Conference on computational intelligence and Security

作者： Liu, Jinzhu Min, Lequan Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Appl Sci & Informat Engn Beijing 100083 Peoples R China

ISBN: (纸本)9780769530734

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions. In this paper, the Selected Objects Extraction (SOE) CNN was generated to Directional Extraction (DE) CNN which enhance the capabilities of CNNs and improve their efficiency. Based on analytical approach, a theorem of designing robust templates for DE CNNs was established, which provides parameter inequalities to determine parameter intervals for implementing the corresponding functions. Several examples are provided to illustrate the effectiveness of the theorem for extracting selected objects directionally in binary images.

关键词： Video signal processing

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