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检索条件"机构=Computer Vision and Image Processing Laboratory Electrical and Computer Engineering"

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Bacterial foraging algorithm for neutron radiography image quality improvement

Bacterial foraging algorithm for neutron radiography image q...

引用

International Multi-Conference on Systems, Signals and Devices

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

ISBN: (纸本)9781424475346

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. ©2010 IEEE.

关键词： image reconstruction

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Starfruit image Segmentation Based on YCbCr Color Space

Starfruit Image Segmentation Based on YCbCr Color Space

引用

International IEEE Conference on Signal-image Technologies and Internet-Based System

作者： R. Amirulah M.M. Mokji Z. Ibrahim U.U. Sheikh Computer Vision Video and Image Processing (CvviP) Faculty of Electrical Engineering UTM Skudai Johor Malaysia

Segmentation in a classification system plays an important role as it will determine the region of interest for the classification. In this work, the segmentation of star fruit is performed in YCbCr color space. Because of the background of the image to be classify has 2 different colors, which are black and white, the segmentation is obtained using a fixed threshold value for the Cb component. This is because the value for Cb is equivalent in black, white or gray scale pixels. By fixing the threshold for Cb component, the region of interest (ROI) can easily be determined and then proceeded to the classification process. The results in this paper show that the ROI can be determined completely by this method.

关键词： image color analysis image segmentation Object segmentation Inspection Indexes Gray-scale Field programmable gate arrays

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Character-based car plate detection and localization

Character-based car plate detection and localization

引用

International Symposium on Signal processing and Its Applications (ISSPA)

作者： S. Setumin U.U. Sheikh S.A.R Abu-Bakar Computer Vision Video and Image Processing Research Lab Department of Microelectronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia

In this paper we address the issue of locating non-standard Malaysian car license plate. Instead of searching the region for the plate, we directly locate the alphanumeric characters of the car plate. In this manner, we remove issues such as plate size variations and plates on black colored vehicles. Our main goal is to locate and extract the alphanumeric characters of Malaysian special plates. These special plates do not follow the normal standard car plates' format as they may contain italic, cursive and connected letters, and of different fonts. Using several parameters such as pixel compactness, angles, and projection histogram, we use ruled-based technique to locate and detect these special characters of the car plates. The results have shown that we are able to automatically locate with an accuracy of 95%.

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Applying deep-layered clustering to mammography image analytics

Applying deep-layered clustering to mammography image analyt...

引用

2010 Biomedical Science and engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN

作者： Rose, Derek C. Arel, Itamar Karnowski, Thomas P. Paquit, Vincent C. Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN 37996 United States Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831 United States

ISBN: (纸本)9781424467143

This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem;minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and health institutions. This research examines a segmented look at mammograms for computer aided detection with the goal of reliably labeling regions of interest requiring the attention of a radiologist. Classification is achieved by employing the building blocks, namely unsupervised clustering, of a deep learning architecture in tandem with a standard feed-forward neural network. Early results show promise for creating a classification engine that handles high-dimensional data with minimum engineering of image features, with a per-image patch sensitivity of 0.96 and specificity of 0.99. © 2010 IEEE.

关键词： Clustering algorithms

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Implementation of Starfruit Maturity Classification Algorithm on Embedded System

Implementation of Starfruit Maturity Classification Algorith...

引用

2010 2nd International Conference on Signal processing System(2010年信号处理系统国际会议 ICSPS 2010)

作者： R.Amirulah M.M.Mokji Z.Ibrahim Computer Vision Video and Image Processing (CvviP)Faculty of Electrical Engineering 81310 UTM Skudai Johor Malaysia

This paper presents an implementation of starfruit maturity classification algorithm in an embedded real-time system. In order to do classification for the starfruit the maturity of the starfruit is classified based on new color feature, which is the hue between 2 color component of red and green denoted as C. Field Programmable Gates Array (FPGA) is selected to be the main processor of the system. Not only because of the capability of parallel execution on the algorithm, the FPGA is chosen also because of its low power consumption. The acquisition device for the system is a CMOS camera, which immune to high noise and static low power. From the CMOS camera, the image is transferred into the FPGA through ITU-R 656 Decoder. With YCbCr 4:2:2 color system, firstly the system will segment the starfruit image in order to remove the background. Next, index maturity classification is performed In this work, classification accuracy of 90％ was achieved. The index result is shown at Liquid Crystal DispLay (LCD).

关键词： Real-time system FPGA Color features CMOS ITU-R 656 Decoder YCbCr LCD

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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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9th International Conference on Machine Learning and Applications, ICMLA 2010

作者： Karnowski, Thomas P. Arel, Itamar Rose, Derek Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831 United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN 37996 United States

ISBN: (纸本)9780769543000

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. © 2010 IEEE.

关键词： Hierarchical systems

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Automatic mask creation and feature analysis for detectionof IVDD in canines

Automatic mask creation and feature analysis for detectionof...

引用

International Conference on computer Sciences and Convergence Information Technology (ICCIT)

作者： Jakia Afruz Jesse Phelps Scott E Umbaugh Dominic J. Marino Catherine A. Loughin Computer Vision and Image Processing Laboratory Electrical and Computer Engineering Southern Illionis University Edwardsville Edwardsville IL USA Long Island Veterinary Specialists Plainview NY USA

Intervertebral disc disease (IVDD) is a condition that affects both humans and canines. This disease is painful and degrades the quality of life for those that suffer from it. The current diagnostic methods for this disease are Magnetic Resonance Imaging (MRI) and/or Computed Axial Tomography (CAT scan). Both of these diagnostic methods are expensive and time consuming and involve patients being exposed to radiation and strong magnetic fields. In canines in particular, the amount of time it takes to perform one of these scans makes it necessary to sedate the animal as the patient needs to be still during the diagnostic procedure. Use of thermographic imaging for diagnosis, or at least prescreening, patients for IVDD will save money, time and possibly prevent needless radiation exposure. This paper attempts to show that thermal imaging of patients that are candidates for IVDD may be used diagnostically or as a prescreening method.

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Adaptive Carrier-Sensing for Throughput Improvement in IEEE 802.11 Networks

Adaptive Carrier-Sensing for Throughput Improvement in IEEE ...

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2010 IEEE Global Telecommunications Conference GLOBECOM 2010

作者： Ehsan Haghani Michael N. Krishnan Avideh Zakhor Video and Image Processing Laboratory Department of Electrical Engineering and Computer Science University of California Berkeley USA

As a Carrier Sense Multiple Access (CSMA) network, the performance of IEEE 802.11 networks highly depends on the accuracy of the carrier sensing procedure. However, conventional carrier sensing approaches suffer from the well known hidden and exposed node problems, adversely affecting aggregate throughput of the IEEE 802.11 networks. In this paper, we propose a novel scheme through which each station can adaptively select its Carrier Sense Threshold (CST) in order to mitigate the hidden/exposed node problems. The basic idea behind our approach is for the Access Point (AP) to periodically transmit a Busy/Idle (BI) signal to all the stations. Individual stations then use the BI signal from the AP together with their own local BI signal in order to adjust their CST. We use NS-2 simulations to show that our approach can enhance the aggregate throughput by as much as 50%.

关键词： Throughput Peer to peer computing Bismuth Energy states Aggregates Sensors Adaptation model

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Intrinsic signatures for scanned documents forensics : Effect of font shape and size

Intrinsic signatures for scanned documents forensics : Effec...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Nitin Khanna Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Recently there has been a great deal of interest in using features intrinsic to a data-generating sensor for the purpose of source identification. Numerous methods have been proposed for different problems related to sensor forensics, such as source camera identification using sensor noise as intrinsic signatures, printer identification using banding artifacts as intrinsic signatures and so on. The goal of our work is to identify the scanner used for generating a scanned (digital) version of a printed (hard-copy) document. In this paper we do extensive analysis of the effect of font shape and size on our recently proposed, texture analysis based intrinsic signatures for scanned documents. Some improvements in these intrinsic signatures are proposed to make them robust to font shapes and sizes.

关键词： Forensics Shape Cameras image sensors Support vector machines Support vector machine classification Sensor phenomena and characterization Feature extraction Linear discriminant analysis image generation

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Comparison of voxel-based morphometry (VBM) and tractography of diffusion tensor MRI (DT-MRI) in temporal lobe epilepsy

Comparison of voxel-based morphometry (VBM) and tractography...

引用

2010 18th Iranian Conference on electrical engineering, ICEE 2010

作者： Afzali, Maryam Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9781424467600

Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain that harbor seizure focus is a critical step towards treatment of TLE patients. Diffusion tensor imaging (DTI) is a non-invasive method that we use in this work to determine damaged regions of white matter in the brain. To this end, we apply two methods, voxel based morphometry (VBM) and fiber tracking. We use these methods along with two anisotropy maps (EAR and FA) extracted from the DTI data. We use DTI data of 31 subjects (12 normal subjects and 19 TLE patients) for testing and evaluation studies. The VBM method shows that TLE is associated with reduction of both of FA and EAR values in several regions within the temporal lobes. The tractography method shows abnormal diffusion characteristics in three fiber bundles (fornix, uncinate fasciculus, and cingulum). The results show that the EAR maps are less sensitive to noise than the FA maps and the EAR values change more than the FA values in TLE. ©2010 IEEE.

关键词： Diffusion tensor imaging

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