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An adapted point based tracking for vehicle speed estimation in linear spacing

An adapted point based tracking for vehicle speed estimation...

引用

International Conference on computer and Communication engineering, ICCCE

作者： H. A. Rahim R. B. Ahmad A. S. M. Zain U. U. Sheikh School of Computer and Communication Engineering Universiti Malaysia Pedis Kangar Perlis Malaysia Computer Vision Video and Image Processing Research Laboratory (CVVIP) Department of Microelectronics and Computer Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

Vehicle velocity estimation is an important aspect of intelligent transportation systems. Normally velocity is estimated using dedicated laser speed traps and Doppler radars. Recently, the use of cameras is becoming more common for the purpose of traffic surveillance and smart surveillance system. It is thus the aim of this paper to propose a method for vehicle speed estimation using these existing video cameras. In this paper, we propose a vehicle speed estimation method from video analysis. The method proposed contains several steps; image preprocessing, centroid extraction and tracking. The proposed method transforms the 2D image points into a 3D virtual world to obtain actual vehicle position in 3D space. This is to account for perspective distortion commonly seen in images. Using these 3D points and measuring the time for displacement, the vehicle speed is obtained. Experimental results have shown that the proposed method gives accurate velocity estimation.

关键词： Vehicles Estimation Surveillance Tracking Pixel Cameras Three dimensional displays

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A LOW COMPLEXITY METHOD FOR DETECTION OF TEXT AREA IN NATURAL imageS

A LOW COMPLEXITY METHOD FOR DETECTION OF TEXT AREA IN NATURA...

引用

IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Katherine L. Bouman Golnaz Abdollahian Mireille Boutin Edward J. Delp Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (纸本)9781424442959

We propose a low complexity method for segmentation of text regions in natural images. This algorithm is designed for mobile applications (e.g. unmanned or hand-held devices) in which computational and energy resources are limited. No prior assumption is made regarding the text size, font, language, character set or the camera angle. However, the text is assumed to be located on a piecewise homogeneous background with a contrasting color. We have deployed our method on a Nokia N800 Internet tablet as part of a system for automatic detection and translation of outdoor signs. Our experiments show that the 0.3 megapixel images taken by the phone camera can be accurately segmented within the device in a fraction of a second.

关键词： Text Detection Text Segmentation Sign Detection Mobile Devices Mobile devices text segmentation images lens angle hand-held equipment Mobile Applications UNMANNED automatic detection Energy sources

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Testing cross-platform streaming of video games over wired and wireless LANs

Testing cross-platform streaming of video games over wired a...

引用

24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010

作者： Jurgelionis, A. Bellotti, F. De Gloria, A. Laulajainen, J.-P. Fechteler, P. Eisert, P. David, H. Department of Biophysical and Electronic Engineering University of Genoa Genoa Italy Converging Networks Laboratory VTT Technical Research Centre of Finland Oulu Finland Computer Vision and Graphics Image Processing Department Heinrich-Hertz-Institute Fraunhofer-Institute for Telecommunications Berlin Germany R and D Department Exent Technologies Ltd. Petach Tikva Israel

ISBN: (纸本)9780769540191

In this paper we present a new cross-platform approach for video game delivery in wired and wireless local networks. The developed 3D streaming and video streaming approaches enable users to access video games on set top boxes and handheld devices that natively are not capable to run PC games. During the development of the distributed gaming system we have faced a number of challenges and problems posed by the hardware and network limitations. In order to solve these problems we have developed a multilevel testing methodology which is based on user assessment and technical measures for the system under development. In this paper we focus on the technical measures and instrumentation that we use for the system's performance measurement and testing. The benefits of our testing methodology are demonstrated through examples from the development and testing work. © 2010 IEEE.

关键词： Human computer interaction

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Bacterial Foraging Algorithm for neutron radiography image quality improvement

Bacterial Foraging Algorithm for neutron radiography image q...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： S. Saadi A. Kouzou A. Guessoum M. Bettayeb University Ziane Achour of Djelfa Algeria Department of Electronics Signal and image Processing Laboratory USDB Burgas Algeria Department of Electrical and Computer Engineering University of Sharjah UAE

We want to implement a new optimal approach for image restoration problem, which is useful for neutron radiography images enhancement to assist the physician interpreter on his evaluation. Our approach is based on using swarm intelligence optimization algorithms to solve a least squares minimization ill-posed problem, using Bacterial Foraging Optimization (BFO) Algorithm. To get smoothed images in presence of noise, a Laplacian constraint is introduced for regularization purpose.

关键词： Wiener filter image restoration Radiography Strontium

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A vector SIFT operator for interest point detection in hyperspectral imagery

A vector SIFT operator for interest point detection in hyper...

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Workshop on Hyperspectral image and Signal processing: Evolution in Remote Sensing, WHISPERS

作者： Leidy P. Dorado-Muñoz Miguel Velez-Reyes Amit Mukherjee Badrinath Roysam Laboratory of Applied Remote Sensing and Image Processing University of Puerto Rico Puerto Rico Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute NY USA

An algorithm for automated extraction of interest points (IP) in hyperspectral images is presented. IP are features of the image that capture information from its neighbors and are distinctive and stable under translation and rotation. IP operators for gray level images were proposed more than a decade ago and have since been studied extensively. IP are helpful in data reduction to reduce the computational burden of various algorithms by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. The vector SIFT approach extends Lowe's IP operator that uses local extrema of Difference of Gaussian at multiple scales to detect interest point in gray level images by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Vector anisotropic diffusion is used for scale-space generation which enhances edges and improves IP detection. Experiments with hyperspectral images of different spatial resolutions and evaluation of IP found based on image registration is presented.

关键词： Hyperspectral imaging Pixel Detectors Feature extraction image edge detection IP networks

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Techniques for automatic magnetic resonance image classification

Techniques for automatic magnetic resonance image classifica...

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International Conference on Genetic and Evolutionary Computing

作者： Chen, Hsian-Min Chen, Shih-Yu Chai, Jyh Wen Chen, Clayton Chi-Chang Wu, Chao-Cheng Ouyang, Yen-Chieh Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions. © 2010 IEEE.

关键词： Support vector machines

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Mahdi Ramezani Amin Heidari Emad Fatemizadeh Hamid Soltanian-Zadeh Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Clustering algorithms Algorithm design and analysis Correlation Approximation methods Independent component analysis Time series analysis Approximation algorithms

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Volume-based magnetic resonance brain image classification

Volume-based magnetic resonance brain image classification

引用

International Conference on Genetic and Evolutionary Computing

作者： Chiou, Yaw-Jiunn Chai, Jyh-Wen Chen, Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Ouyang, Yen-Chieh Su, Wu-Chung Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. © 2010 IEEE.

关键词： Support vector machines

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Deep Spatiotemporal Feature Learning with Application to image Classification

Deep Spatiotemporal Feature Learning with Application to Ima...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Thomas P. Karnowski Itamar Arel Derek Rose Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA

Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results using a recently introduced deep learning architecture called Deep Spatio-Temporal Inference Network (DeSTIN). DeSTIN is a discriminative deep learning architecture that combines concepts from unsupervised learning for dynamic pattern representation together with Bayesian inference. In DeSTIN the spatiotemporal dependencies that exist within the observations are modeled inherently in an unguided manner. Each node models the inputs by means of clustering and simple dynamics modeling while it constructs a belief state over the distribution of sequences using Bayesian inference. We demonstrate that information from the different layers of this hierarchical system can be extracted and utilized for the purpose of pattern classification. Earlier simulation results indicated that the framework is highly promising, consequently in this work we expand DeSTIN to a popular problem, the MNIST data set of handwritten digits. The system as a preprocessor to a neural network achieves a recognition accuracy of 97.98% on this data set. We further show related experimental results pertaining to automatic cluster adaptation and termination.

关键词： computer architecture Training Artificial neural networks Measurement Pixel Machine learning Feature extraction

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A comparative study to select an image deconvolution method

A comparative study to select an image deconvolution method

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： S. Saadi A. Kouzou A. Guessoum M. Bettayeb Department of Electronics University Ziane Achour of Djelfa Algeria Department of Electronics Signal and Image Processing Laboratory USDB Burgas Algeria Department of Electrical and Computer Engineering University of Sharjah UAE

image deconvolution is an important subject in image processing. It is an ill-posed inverse problem, so regularization techniques are used to solve this problem by adding constraints to the objective function. Various popular algorithms have been developed to solve such problem. This paper studies various approaches to the nonlinear degraded images restoration problem which are useful in many images enhancement applications. Swarm intelligence is applied for total variation (TV) minimization, instead of the standard Tikhonov regularization method which is often used. In this work, we attempt to reconstruct or recover corrupted images that have been degraded during acquisition; using some a priori knowledge of the degradation phenomenon. The truncated singular value decomposition (TSVD) method is also considered for image deconvolution in this paper. A comparison between these methods made on examples is included.

关键词： Noise measurement

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