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International Symposium on Telecommunications

作者： Mohammad Shams Esfand Abadi Zahra Saffari Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University Adaptive Systems and Image Processing Research Laboratory Shahid Rajaee Teacher Training University

ISBN: (纸本)9781467320726

In this paper, we present the distributed selective partial update affine projection algorithm (dSPU-APA), the distributed dynamic selection affine projection algorithm (dDS-APA), and dSPU-DS-APA to solve distributed estimation problem over adaptive diffusion networks. In dSPU-APA, the filter coefficients are partially updated during the adaptation. For dDS-APA, the input regressors are dynamically selected at each iteration. Diffusion cooperation schemes have been shown to provide good performance in these algorithms. The good performance of these algorithms is demonstrated by presenting several simulation results.

关键词： Distributed selective partial affine projection algorithm Dynamic selection Distributed estimation Adaptive diffusion networks Diffusion cooperation

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Automatic organs' detection in WCE

Automatic organs' detection in WCE

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Omid Haji-Maghsoudi Alireza Talebpour Hamid Soltanian-Zadeh Navid Haji-maghsoodi Department of Radiation Medicine Shahid Beheshti University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Hospital Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Mechatronics Sharif University of Tehran Tehran Iran

Wireless capsule endoscopy (WCE) views the entire gastrointestinal (GI) tract. A main problem associated with this novel device is that too many frames must be reviewed by physicians. Thus it is essential to find an automatic and intelligent method to help physicians. One of the problems in WCE is its difficulty to distinguish among different organ's tissues. So, we introduce two novel algorithms which are able to classify main organs (among esophagus, stomach, small bowel and colon) in WCE's frames. In order to obtain our aim, we use statistic features (Haralick features) and non-statistic features (different diagrams and Gabor filter banks), colored features and non-colored features. Our experimental studies indicate good results that are shown in conclusion.

关键词： Level set Medical services Gabor filters Active contours Wireless communication Optical wavelength conversion Esophagus

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Car plate segmentation based on morphological and labeling approach

Car plate segmentation based on morphological and labeling a...

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2011 Computation and Communication Technologies: 3rd International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2011

作者： Mousavi, Seyed Mojtaba Shahdi, Seyed Omid Abu-Bakar, S.A.R. Computer Vision Video and Image Processing Research Lab Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia

ISBN: (纸本)9781629934563

Automatic car plate extraction is one of the interesting research areas in surveillance applications such as traffic control, parking lot and detection of stolen vehicles. This topic is divided in two main parts, firstly license plate localization, and secondly, license plate characters recognition. In this paper merely the first part is taken into consideration by means of morphological operations and labeling algorithm. Moreover, performance of proposed method is investigated to unveil the ability in extracting of the license plate. Space between characters and also height and width of the license plate are fundamental issues in morphological operations along with dimension of characters in labeling algorithm. Results for this method are promising even in tough conditions.

关键词： Edge detection

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Erratum to: Methods for evaluating medical tests and biomarkers

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Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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The texture classification using the fusion of decisions from different texture classifiers

The texture classification using the fusion of decisions fro...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology

作者： Pharsook, S. Kasetkasem, T. Larmsrichan, P. Siddhichai, S. Chanwimaluang, T. Isshiki, T. Department of Electrical Engineering Kasetsart University Thailand Image Processing Laboratory National Electronics and Computer Technology Center Thailand Department of Communication and Integrated Systems Tokyo Institue of Technology Japan

ISBN: (纸本)9781457704246

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%. © 2011 IEEE.

关键词： Support vector machines

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Model-based multiview stereo via level sets with statistical shape prior

Model-based multiview stereo via level sets with statistical...

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IEEE International Conference on image processing

作者： Moumen El-Melegy Nagi Al-Ashwal Aly A. Farag Electrical Engineering Department Assiut University Asyut Egypt Computer Vision and Image Processing Laboratory University of Louisville KY USA

In this paper, we propose a methodology to incorporate 3D shape prior information in multi-view stereo. This is important for applications that deal with specific category of objects. The methodology is based on a new formulation of a level-set based energy functional. Shape prior model is then embedded in the energy functional to allow the reconstruction of an object with shape variations consistent with the training model examples. Several experiments to evaluate the proposed methodology are presented.

关键词： Shape image reconstruction Level set Stereo vision Solid modeling Training Ellipsoids

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Elastic Shape Registration Using an Incremental Free Form Deformation Approach with the ICP Algorithm

Elastic Shape Registration Using an Incremental Free Form De...

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Canadian Conference on computer and Robot vision, CRV

作者： Hossam Abdelmunim Aly A. Farag Computer & Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Vision and Image Processing Laboratory Electrical & Computer Engineering Department University of Louisville KY USA

Shape registration is one of the most challenging problems in computer vision and medical imaging. The process is affected by the way the shape is represented and the form of transformation used to move the source shape. Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as a criteria to establish point correspondences in each level of the IFFD framework. The free form deformation (FFD) is well known in the literature and works by forming a lattice of control points that can move and hence deform the domain grid points smoothly and uniformly under some shape constrains. The control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.

关键词： Shape Lattices Iterative closest point algorithm image resolution Mathematical model Equations Energy resolution

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Elastic shape registration using an incremental free form deformation approach with the ICP algorithm

Elastic shape registration using an incremental free form de...

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IEEE International Conference on image processing

作者： Hossam Abdelmunim Aly A. Farag Computer & Systems Engineering Department Ain Shams University Cairo Egypt Computer Vision and Image Processing Laboratory Electrical & Computer Engineering Department University of Louisville Louisville KY USA

Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point- based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as criteria to establish point correspondences in each level of the IFFD framework. The IFFD control lattice resolution is in- creased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.

关键词： Shape Lattices Iterative closest point algorithm Equations Spline Mathematical model Three dimensional displays

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Frequency domain feature-based face recognition technique for different poses and low-resolution conditions

Frequency domain feature-based face recognition technique fo...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Seyed Omid Shahdi S.A.R. Abu-Bakar Computer Vision Video and Image Processing (CvviP) Research Laboratory Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

Pose variations are known to give real challenges in face recognition system. In this paper we proposed a method to recognize non-frontal faces with high performance by relying only on single full frontal gallery faces. By utilizing only small regions of the face or patches, we compute the Fourier coefficients of these patches for each image and transform them into a single vector. Hence, instead of comparing and matching pixels values we use these vectors to form a linear relationship which is then used to estimate the frontal face vector and then compare it with the actual frontal feature vector. The results show an average performance accuracy of 90% across all pose.

关键词： Face Face recognition Databases Feature extraction Training Vectors

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Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest

Evaluation of geometric feature descriptors for detection an...

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IEEE International Symposium on Biomedical Imaging

作者： Amal Farag Asem Ali James Graham Aly Farag Salwa Elshazly Robert Falk Computer Vision and Image Processing Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical Imaging Division Jewish Hospital Louisville KY USA

This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect candidate nodules based on optimum similarity measured by the normalized cross-correlation (NCC). Geometric feature descriptors (e.g., SIFT, LBP and SURF) are applied to the output of the detection step, in order to extract features from the nodule candidates, for further enhancement of output and possible reduction of false positives. Results on the clinical ELCAP database showed that the descriptors provide 2% enhancements in the specificity of the detected nodule above the NCC results when used in a k-NN classifier. Thus quantitative measures of enhancements of the performance of CAD models based on LDCT are now possible and are entirely model-based. Most importantly, our approach is applicable for classification of nodules into categories and pathologies.

关键词： Feature extraction Lungs Training Databases Solid modeling Principal component analysis Computed tomography

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