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

共 784 条记录，以下是301-310 订阅

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Sport Video Classification Using an Ensemble Classifier

Sport Video Classification Using an Ensemble Classifier

引用

Iranian Machine vision and image processing (MVIP)

作者： Mohamad Hoseyn Sigari Samaneh Abbasi Sureshjani Hamid Soltanian-Zadeh Control & Intelligent Processing Center of Excellence (CIPCE) School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA

Sport video classification is an application of video analysis which can be useful in video indexing and retrieval. In this article, a new method for sport video classification using ensemble classifier is proposed. The proposed method uses 6 features: 3 dominant colors, dominant gray level, cut rate and motion rate. These features are classified by 4 simple classifiers in an ensemble classifier: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), Decision Tree (DT) and Probabilistic Neural Network (PNN). To combine the output of simple classifiers and make final decision, weighted majority vote is used while the weight of each simple classifier is equal to corresponding correct classification rate (CCR). Experimental result shows that the CCR of proposed system is 78.8%. In this experiment, 104 clips in 7 different sport classes are used: football, basketball, tennis, swimming, futsal, ski and box.

关键词： Feature extraction Color image color analysis Classification algorithms Accuracy Artificial neural networks Histograms

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Traffic speed measurement using Spatio-Temporal model and frequency domain analysis

Traffic speed measurement using Spatio-Temporal model and fr...

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

作者： Arucha Rungchokanun Vutipong Areekul Supakorn Siddhichai Hiroaki Kunieda Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Kasetsart University Thailand Image Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Communication and Integrated Systems Tokyo Institute of Technology Meguro Tokyo Japan

In traffic monitoring applications, traffic speed is an important parameter of traffic management. The method for traffic speed measurement using video based on Spatio-Temporal (ST) model and frequency domain analysis is proposed in this paper. This method is designed to be able to measure the traffic speed in every pattern of road. The novel of proposed method is unfolding the edge information on ST model and analyse them in frequency domain to determine traffic speed. The proposed method is evaluated by compare with actual speed. The traffic speed is measured accurately 97.1%.

关键词： Analytical models Sensors Pixel image sensors Monitoring

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Universal Steganalysis Based on Local Prediction Error in Wavelet Domain

Universal Steganalysis Based on Local Prediction Error in Wa...

引用

International Conference on Intelligent Information Hiding and Multimedia Signal processing (IIHMSP)

作者： Anahita Shojaei-Hashemi Mostafa Mehdipour-Ghazi Shahrokh Ghaemmaghami Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electronics Research Institute Sharif University of Technology Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes.

关键词： Equations Mathematical model Payloads Transform coding Correlation Educational institutions Reliability

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Variational approach for segmentation of lung nodules

Variational approach for segmentation of lung nodules

引用

IEEE International Conference on image processing

作者： Amal A. Farag Hossam Abdelmunim James Graham Aly A. Farag Salwa Elshazly Sabry El-Mogy Mohamed El-Mogy Robert Falk Sahar Al-Jafary Hani Mahdi Rebecca Milam Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY USA Computer & Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt School of Medicine Mansoura University Egypt FJewish Hospital and 3DR Louisville KY USA Department of Radiology University of Louisville USA

Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper presents an approach for segmentation of lung nodules detected by a prior step. First, regions around the detected nodules are segmented; using automatic seed point placement levels sets. The outline of the nodule region is further improved using the curvature characteristics of the segmentation boundary. We illustrate the effectiveness of this method for automatic segmentation of the Juxta-pleural nodules.

关键词： Lungs image segmentation Computed tomography Level set Shape Conferences Databases

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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

引用

Middle East Conference on Biomedical engineering (MECBME)

作者： M. Parsa Hosseini Hamid Soltanian-Zadeh Shahram Akhlaghpoor Department of Electrical Engineering Islamic Azad University Tehran Iran Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system.

关键词： Lungs Computed tomography Diseases image segmentation Biomedical imaging Gaussian distribution

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

引用

Middle East Conference on Biomedical engineering (MECBME)

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).

关键词： Lungs Computed tomography Diseases Active contours image segmentation Charge carrier processes

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PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COGNITIVE RADIO SYSTEMS WITH INTERFERENCE CONSTRAINTS

PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COG...

引用

2011 4th IEEE International Conference on Broadband Network and Multimedia Technology

作者： Saeed Motiian Mohammad Aghababaie Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Colleague of EngineeringUniversity of TehranTehran 14399-57131Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences(IPM)Tehran 19548-56316 Iran Image Analysis Laboratory Radiology DepartmentHenry Ford HospitalDetroitMichigan 48202USA

Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service（QoS）for both primary and secondary *** defined as functions of the signal-to-interference-plus-noise ratio（SINR） are matched for each secondary user which meets Nash’s *** general,the region of utilities that meets the constraints is *** is possible to make simplifications,generate a convex region,and then use common convex optimization approaches to obtain a ***,Particle Swarm Optimization（PSO）does not need such simplifications and thus its results are superior to those of the convex optimization *** is an evolutionary algorithm based on social intelligence,utilized in many optimization *** is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.

关键词： Cognitive radio Power allocation Particle Swarm Optimization（PSO）.

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Unscented transformation for depth from motion-blur in videos

Unscented transformation for depth from motion-blur in video...

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2010 IEEE computer Society Conference on computer vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Paramanand, C. Rajagopalan, A.N. Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai 600 036 India

ISBN: (纸本)9781424470297

In images and videos of a 3D scene, blur due to camera shake can be a source of depth information. Our objective is to find the shape of the scene from its motion-blurred observations without having to restore the original image. In this paper, we pose depth recovery as a recursive state estimation problem. We show that the relationship between the observation and the scale factor of the motion-blur kernel associated with the depth at a point is nonlinear and propose the use of the unscented Kalman filter for state estimation. The performance of the proposed method is evaluated on many examples. © 2010 IEEE.

关键词： State estimation

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.

关键词： Tumors Anisotropic magnetoresistance Diffusion tensor imaging Support vector machines Feature extraction Artificial neural networks

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Inferring image transformation and structure from motion-blurred images

Inferring image transformation and structure from motion-blu...

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2010 21st British Machine vision Conference, BMVC 2010

作者： Chandramouli, Paramanand Rajagopalan, A.N. Image Processing and Computer Vision Lab. Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)1901725405

This paper deals with the problem of estimating structure of 3D scenes and image transformations from observations that are blurred due to unconstrained camera motion. Initially, we consider a fronto-parallel planar scene and relate the reference image of the scene to its motion-blurred observation by finding the reference image transformations. The blur kernel at every image point can be determined from these transformations. For 3D scenes, the extent of blurring in the image is related to the camera motion as well as the scene structure. We propose a technique to estimate the scene depth with the knowledge of the estimated image transformations. The proposed method is validated by testing on real and synthetic experiments. © 2010. The copyright of this document resides with its authors.

关键词： Cameras

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