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共 276 条记录，以下是91-100 订阅

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A Neural Network based Approach for the Vehicle Classification

A Neural Network based Approach for the Vehicle Classificati...

引用

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

作者： Anshul Goyal Brijesh Verma Central Queensland University Rockhampton QLD Australia

This paper presents a neural network based approach for vehicle classification. The proposed vehicle classification approach extracts various features from a vehicle image, normalises and classifies them into one of the known classes. It is based on structural features and a direct solution training method. The preliminary experiments on training and testing of 4 types of vehicles patterns were conducted. The experimental results are very promising and demonstrate the effectiveness and usefulness of the proposed approach

关键词： Neural networks Intelligent vehicles Surveillance computational intelligence Vehicle detection Acoustic sensors signal processing Roads Terrorism Real time systems

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2009 ieee symposium on computational intelligence for Multimedia signal and Vision processing, CIMSVP 2009 - Proceedings

2009 IEEE Symposium on Computational Intelligence for Multim...

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2009 ieee symposium computational intelligence for Multimedia signal and Vision processing, CIMSVP 2009

ISBN: (纸本)9781424427710

The proceedings contain 14 papers. The topics discussed include: a model of angle selectivity in area V2 with local divisive normalization;maximizing neural responses leads to sensori-motor coordination of binocular vergence;cortical columns: building blocks for intelligent systems;character recognition with two spiking neural network models on multicore architectures;a car detection system based on hierarchical visual features;recurrent network-based face recognition using image sequences;3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts;a hybrid method for facial recognition systems;binary image registration using cellular simultaneous recurrent networks;quality-aware selection of quality factor and scaling parameters in JPEG image transcoding;and relevance tuning in content-based retrieval of structurally-modeled images using particle swarm optimization.

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Novel Variational Approach for Generalized signal Dependent Noise Removal 11

Novel Variational Approach for Generalized Signal Dependent ...

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11th International symposium on computational intelligence and Design (ISCID)

作者： Chen, Deshan Teng, XuanXuan Wuhan Univ Technol Natl Engn Res Ctr Water Transport Safety Intelligent Transportat Syst Res Ctr Wuhan 430063 Hubei Peoples R China Wuhan Polytech Univ Sch Elect & Elect Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781538685273

image denoising is fundamental in image processing and computer vision field, where the assumption of a signal independent additive white Gaussian noise model dominates the most state-of-the-art denoising algorithms. In this paper, we propose a novel variational approach that addresses more challenging generalized signal-dependent noise model, which is more flexible and practical for real imaging scenarios. The generalized noise model is decomposed into two independent procedures by introducing an intermediate latent variable, with which the variational model is thus constructed. Moreover, the popular second-order total generalized variation regularizer is introduced into our variational model. To effectively solve the variational problem, we first simply the objective functional by substituting one of the corresponding Euler-Lagrange equations and thus eliminating the intermediate variable. A semi-explicit iterative scheme is present in the proximity operation so as to overcome the difficulty of the high nonlinearity involved in the data term when applying primal and dual hybrid gradient algorithm. Experimental results validate the effectiveness and efficiency of our method.

关键词： signal dependent noise total generalized variation variational method image denoising primal and dual hybrid gradient

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An intelligent hardware structure for impulse noise suppression 3

An intelligent hardware structure for impulse noise suppress...

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3rd International symposium on image and signal processing and Analysis (ISPA)

作者： Louverdis, G Andreadis, I Papamarkos, N Democritus Univ Thrace Dept Elect & Comp Engn Sect Elect & Informat Syst Technol GR-67100 Xanthi Greece

ISBN: (纸本)953184061X

In this paper an intelligent hardware module suitable for the computation of an adaptive median filter (AMF) is presented. The proposed digital hardware structure is pipelined and parallel processing is used to minimize computational time. It is capable, of processing gray-scale images of 8-bit resolution with 3x3 or 5x5-pixel image neighborhoods as options for the computation of the filter output. However, the system can be easily expanded to accommodate windows of larger sizes. The function of the proposed circuitry is to detect the existence of impulse noise in an image neighborhood and apply the median filter operator only when necessary. Moreover, the noise detection procedure can be customized so that a range of pixel values is considered as impulse noise. In this way, the integrity of edge and detail information of the image under process is preserved and blurring is avoided. The proposed digital structure was implemented in FPGA and it can be used in industrial imaging applications, where fast processing is of the utmost importance. As an example, the time required to perform filtering of a grayscale image of 260x244 pixels is approximately 7.6 msec. The typical system clock frequency is 65 MHz.

关键词： Adaptive filters Circuit noise computational intelligence Concurrent computing Gray-scale Hardware image edge detection image resolution Intelligent structures Parallel processing

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Recurrent Network-Based Face Recognition Using image Sequences

Recurrent Network-Based Face Recognition Using Image Sequenc...

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ieee symposium on computational intelligence for Multimedia signal and Vision processing

作者： Ren, Yong Iftekharuddin, Khan M. White, William E. Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)9781424427710

In this work, we propose a novel method for face recognition with large pose variations in image sequences using a Cellular Simultaneous Recurrent Network (CSRN). The pose problem is still a daunting challenge in face recognition. If the image sequences are obtained from different viewpoints in a surveillance type of application, the face recognition rate drops significantly. We formulate the recognition problem for face image sequences with large pose variation as an implicit temporal prediction task for CSRN. Further, to reduce the computational cost, we obtain eigenfaces for a set of image sequences for each person and use these reduced pattern vectors as the input to the CSRN. The CSRN is trained by this pattern vector, and each CSRN teams how to associate each face class/person in the training phase. When a new face is encountered, the corresponding image sequence is projected to each eigenface space to obtain the test pattern vectors. The Euclidian distances between successive frames of test and output pattern vectors indicate either a match or mismatch between the two corresponding face classes, We extensively evaluate our CSRN-based face recognition technique with 5 persons using publicly available VidTIMIT Audio-Video face dataset [1]. In order to verify the performance of the CSRN, we also implement an Elman neural network for comparison. Our simulation shows that for this VidTIMIT Audio-Video face dataset with large pose variation, we can obtain an overall 65% (for rank 1) or 75% (for rank 2) face recognition accuracy better than the 55%(rank 1) recognition accuracy of Elman neural network.

关键词： Face recognition

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A New Invariant Descriptor For Shape Representation And Recognition

A New Invariant Descriptor For Shape Representation And Reco...

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

作者： Xiying Wang Guozhong Dai Institute of Software Chinese Academy and Sciences Beijing China

Traditional Fourier descriptor suffers the drawback of being sensitive to translation, rotation and scale transform. This paper presents a new method of shape representation, polygon Fourier descriptor (PFD), which can achieve the invariance on translation, rotation and scale transform naturally and without need of a process of normalization. By applying polygonal approximation to the contour firstly, the contour can be represented more efficiently. Moreover, PFD is robust enough to represent the contour even if some transformations happened to it. Finally, we apply PFD to the recognition of static gestures and obtain satisfying results

关键词： Shape Phase frequency detector Fourier transforms image recognition image processing computational intelligence signal processing Computer errors Robustness Object recognition

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Non-pixel Robot Stereo

Non-pixel Robot Stereo

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

作者： Tyler C. Folsom University of British Columbia Vancouver BC Canada

The 2005 DARPA Grand Challenge was a 213 Km robot race across the Mojave Desert between Nevada and California. Our team attempted to run the course using only stereo vision. I designed a novel real-time large-image stereo vision system that uses sparse stereo and is biologically inspired. It uses a filter similar to that used in primary visual cortex to locate small line segments. These are then arranged to form a polyline representation of the scene and estimate the depth. The technique was capable of nine frames per second of a megapixel image on a single processor. Accuracy was questionable and the robot did not compete in the race for other reasons

关键词： Gabor filters Biology computing Stereo vision Intelligent robots signal processing algorithms image edge detection Retina Polynomials computational intelligence Biomedical signal processing

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Intensity / Correlation Thresholding of FMRI Data: Data-driven Regions of Interest using Bridge Voxels

Intensity / Correlation Thresholding of FMRI Data: Data-driv...

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

作者： M. D. Alexiuk N. J. Pizzi W. Pedrycz National Research Council Institute of Biodiagnostics Winnipeg MAN Canada

Analysis of functional magnetic resonance imaging data considers temporal correlation of hemodynamic intensity patterns to a known activation paradigm as well as spatial regions of similar responses. Default regions of interest are often obtained by analyst-directed thresholding of intensity or correlation values. This paper presents a method to determine data-driven regions of interest using image erosion to identify structurally significant components in the default regions, namely bridge voxels

关键词： Bridges Hemodynamics computational intelligence Magnetic resonance imaging Robustness Blood flow Clustering algorithms signal processing algorithms Biomedical signal processing Councils

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Learning computationally Efficient Approximations of Complex image Segmentation Metrics

Learning Computationally Efficient Approximations of Complex...

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8th International symposium on image and signal processing and Analysis (ISPA)

作者： Minervini, Massimo Rusu, Cristian Tsaftaris, Sotirios A. IMT Inst Adv Studies Lucca Italy

ISBN: (纸本)9789531841948;9789531841870

image segmentation metrics have been extensively used in the literature to compare segmentation algorithms among each other, or relative to a ground-truth segmentation. Some metrics are easy to compute (e.g., Dice, Jaccard), others are more accurate (e.g., the Hausdorff distance) and may reflect local topology, but they are computationally demanding. While certain attempts have been made to create computationally efficient implementations of such complex metrics, in this paper we approach this problem from a radically different viewpoint. We construct approximations of a complex metric (e.g., the Hausdorff distance), combining a small number of computationally lightweight metrics in a linear regression model. We also consider feature selection, using sparsity inducing strategies, to restrict the number of metrics employed significantly, without penalizing the predictive power of the model. We demonstrate our methodology with image data from plant phenotyping experiments. We find that a linear model can effectively approximate the Hausdorff distance using even a few features. Our approach can find many applications, but is largely expected to benefit distributed sensing scenarios where the sensor has low computational capacity, whereas centralized processing units have higher computational capabilities.

关键词： approximation theory image segmentation learning (artificial intelligence) regression analysis topology

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A graph-based signal processing approach for low-rate energy disaggregation

A graph-based signal processing approach for low-rate energy...

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2014 ieee symposium on computational intelligence for Engineering Solutions, CIES 2014

作者： Stankovic, Vladimir Liao, Jing Stankovic, Lina Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XW United Kingdom

ISBN: (纸本)9781479945108

Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches. © 2014 ieee.

关键词： Domestic appliances

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