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Proceedings of the 2016 International Conference on image processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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Multi-spectral document image binarization using image fusion and background subtraction techniques

Multi-spectral document image binarization using image fusio...

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作者： Mitianoudis, Nikolaos Papamarkos, Nikolaos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece

ISBN: (纸本)9781479957514

In this paper, the authors exploit a multispectral image representation to perform more accurate document image binarisation compared to previous color representations. In the first stage, image fusion is employed to create a 'document' and a 'background' image. In the second stage, the FastICA algorithm is used to perform background subtraction. In the third stage, a spatial kernel K-harmonic means classifier binarizes the FastICA output. The proposed system outperforms previous efforts on document image binarization. © 2014 IEEE.

关键词： image fusion

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DeWAFF: A novel image abstraction approach to improve the performance of a cell tracking system

DeWAFF: A novel image abstraction approach to improve the pe...

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： S. Calderón A. Sáenz R. Mora F. Siles I. Orozco M.E. Buemi Department of Electrical Engineering School of Engineering Cancer-Lab Universidad de Costa Rica San José Costa Rica Image Processing and Computer Vision Group Universidad de Buenos Aires Buenos Aires Argentina

This paper presents a new image abstraction approach, aiming to improve typical image related pattern recognition tasks such as segmentation, tracking, and classification. The proposed image abstraction framework performs image denoising and homogeneous region simplification, along with border and region enhancement. The proposed framework consists in a novel generalized approach of common weighted averaging denoising algorithms mixed with Unsharp Masking (USM) border enhancement techniques, to avoid typical USM artifacts as ringing. Results of the different configurations within the image abstraction framework for a cell tracking application are presented.

关键词： image edge detection Noise reduction Histograms AWGN Pattern recognition image segmentation

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Development of young oil palm tree recognition using Haar- based rectangular windows

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IOP Conference Series: Earth and Environmental Science 2016年第1期37卷

作者： S Daliman S A R Abu-Bakar S H Md Nor Azam Computer Vision Video and Image Processing (CvviP) Research Lab Department of Electronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor MALAYSIA. Sime Darby Research Sdn. Bhd. Jalan Pulau Carey 42960 Pulau Carey Selangor MALAYSIA.

This paper presents development of Haar-based rectangular windows for recognition of young oil palm tree based on WorldView-2 imagery data. Haar-based rectangular windows or also known as Haar-like rectangular features have been popular in face recognition as used in Viola-Jones object detection framework. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palm tree. A set of seven Haar-based rectangular windows have been designed to better match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Determination of features for oil palm tree is an essential task to ensure a high successful rate of correct oil palm tree detection. Furthermore, features that reflects the identification of oil palm tree indicate distinctiveness between an oil palm tree and other objects in the image such as buildings, roads and drainage. These features will be trained using support vector machine (SVM) to model the oil palm tree for classifying the testing set and subimages of WorldView-2 imagery data. The resulting classification of young oil palm tree with sensitivity of 98.58% and accuracy of 92.73% shows a promising result that it can be used for intention of developing automatic young oil palm tree counting.

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Improving video-based resting heart rate estimation: A comparison of two methods

Improving video-based resting heart rate estimation: A compa...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Jeehyun Choe Dahjung Chung A. J. Schwichtenberg Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Department of Human Development and Family Studies Purdue University West Lafayette Indiana USA

Recent advancements in video processing make video-based heart rate (HR) estimation possible. Building on this burgeoning field, we adapted an existing video-based HR estimation method to produce more robust and accurate results. Specifically, we removed periodic signals from the recording environment by identifying (and removing) frequency clusters that are present the face region and background. This adaptive passband filter generates more accurate HR estimates and allows other applied filters to work more effectively.

关键词： Heart rate Face Skin Estimation Frequency estimation Passband Cutoff frequency

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MULTI-SPECTRAL DOCUMENT image BINARIZATION USING image FUSION AND BACKGROUND SUBTRACTION TECHNIQUES

MULTI-SPECTRAL DOCUMENT IMAGE BINARIZATION USING IMAGE FUSIO...

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IEEE International Conference on image processing

作者： Nikolaos Mitianoudis Nikolaos Papamarkos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace

ISBN: (纸本)9781479957521

In this paper, the authors exploit a multispectral image representation to perform more accurate document image binarisation compared to previous color representations. In the first stage, image fusion is employed to create a "document" and a "background" image. In the second stage, the FastICA algorithm is used to perform background subtraction. In the third stage, a spatial kernel K-harmonic means classifier binarizes the FastICA output. The proposed system outperforms previous efforts on document image binarization.

关键词： Document image Binarization image Fusion Multi-spectral imaging Independent Component Analysis

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Shape-Based Kidney Detection and Segmentation in Three-Dimensional Abdominal Ultrasound images

Shape-Based Kidney Detection and Segmentation in Three-Dimen...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mahdi Marsousi Konstantinos N. Plataniotis Stergios Stergiopoulos Multimedia Processing Lab in the communication group of the Electrical and Computer Engineering department at the University of Toronto Electrical and Computer Engineering department University of Toronto Defence Research and Development Canada Toronto an Agency for the Candadian department of defence

ISBN: (纸本)9781424479276

Due to recent technical advancements of three-dimensional ultrasound imaging systems, applications of this imaging modality have been expanding from the fetal imaging to cardiac- and abdominal-diagnosis. Among all internal organs, diagnosing the kidney has a paramount importance for rapid bedside treatment of trauma and kidney stone patients using ultrasound images. Although three-dimensional ultrasound provides higher level of structural information of kidneys, manual kidney diagnosis using three-dimensional ultrasound images requires a highly trained medical staff, due to the extensive visual complexity which three-dimensional images contain. Therefore, computer aided automated kidney diagnosis becomes very essential. Due to the challenging problems of ultrasound images, such as speckle noise and inhomogeneous intensity profile, kidney segmentation in three-dimensional ultrasound images has not been sufficiently investigated by researchers. In this paper, we first propose a new automated kidney detection approach using three-dimensional Morison's pouch ultrasound images. Then, we proposed a shape-based method to segment the detected kidneys. A preprocessing step is utilized to overcome the ultrasound challenges. Based on a set of 14 ultrasound volumes, we have evaluated the detection rate of our proposed kidney detection approach which is 92.86%. For kidney segmentation, we compared our proposed method with an existing approach, and the performed statistical analysis strongly validates the superiority of our proposed method with p = 0.000032.

关键词： Kidney Ultrasonic imaging image segmentation Shape Imaging Histograms Three-dimensional displays

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Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR images of Epileptic Patients

Statistical Validation of Automatic Methods for Hippocampus ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Dario Pompili Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering Rutgers University Medical Image Analysis Lab Radiology Department Henry Ford Health System Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the bench-mark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

关键词： Segmentation Hippocampus Magnetic Resonance Imaging (MRI) Comparison Epilepsy

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Support Vector Machine with Nonlinear-Kernel Optimization for Lateralization of Epileptogenic Hippocampus in MR images

Support Vector Machine with Nonlinear-Kernel Optimization fo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Fariborz Mahmoudi Hao Ying Hamid Soltanian-Zadeh Medical Image Analysis Lab. Radiology Department Henry Ford Health System Department of Electrical and Computer Engineering Wayne State University Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods;then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

关键词： Magnetic Resonance Imaging (MRI) Hippocampus Epilepsy Lateralization Support Vector Machine (SVM)

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A generalized directional Laplacian distribution: Estimation, mixture models and audio source separation

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IEEE Transactions on Audio, Speech and Language processing 2012年第9期20卷 2397-2408页

作者： Mitianoudis, Nikolaos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations. In this work, a novel probability distribution is proposed to model multidimensional sparse directional data. The Generalized Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution. The distribution's parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture. The proposed model can solve the general K × L (K

关键词： Source separation

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