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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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2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

作者： Ramezani, Mahdi Heidari, Amin Fatemizadeh, Emad Soltanian-Zadeh, Hamid Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

ISBN: (纸本)9781424466238

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-nearestneighbor 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： Brain

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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...

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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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Gender estimation based on physiological features of the face

Gender estimation based on physiological features of the fac...

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International Symposium on Signal Processing and Its Applications (ISSPA)

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

In this paper, we present a gender estimation technique that will determine whether a person looking at the camera is a male or female. Several facial features extracted from a face are passed to post-image-processing operations before they are sent to a rule-base classifier. We applied our technique to both static passport-like photos as well as several video sequences consisting of a single face each. Our database also contains images of females wearing headscarf as well as bald men. Our initial results showed a very promising outcome. For static face images, we obtained an accuracy of 100% correct gender estimation for females and 98% for males while for video sequences, the accuracy obtained was 87.5% for females and 70% for males.

关键词： Face Hair image resolution Estimation computers Accuracy

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Factorization towards a classifier

Factorization towards a classifier

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Qiang Chen Shuicheng Yan Tian-Tsong Ng Department of Electrical and Computer Engineering National University of Singapore Singapore Computer Vision and Image Understanding Department Singapore

In practice, nonnegative data factorization is often performed for data dimensionality reduction prior to a classification task using a classifier which is effective in low dimensional space such as nearest neighbor classifier. In this work, we propose a novel formulation to learn a multi-class classifier directly through a supervised nonnegative data factorization. This new formulation has the following properties: 1) the nonnegative data matrix is approximated as the product of a nonnegative basis matrix and a coefficient matrix where the nonnegative bases distinctively capture the common characteristics of all classes apart from that specific to individual classes; 2) a regularization term is imposed on nonnegative data factorization so that each datum can be predominantly reconstructed by the common basis vectors and its corresponding class-specific basis vectors; and 3) the coefficient vector for each datum is assumed to be transformed from a mapped kernel space, and the l 2 norm of the class-specific coefficients reveals the relative confidence of classes, which then directly leads to a multi-class classifier. We also present an iterative optimization technique for our formulation and analytically show its convergence property. Extensive experiments on face recognition, head pose estimation, and handwritten digit recognition tasks clearly demonstrate the advantages of the proposed classifier over the conventional two-step approach of nonnegative data factorization followed by a classic classifier.

关键词： Testing Face recognition Nearest neighbor searches Kernel Principal component analysis Independent component analysis Embedded computing computer vision image reconstruction Convergence

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Car Plate Character Extraction and Recognition Using Stroke analysis

Car Plate Character Extraction and Recognition Using Stroke ...

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

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

In this paper we address the issue of recognizing nonstandard Malaysian car license plates. These plates contain nonstandard characters such as italic, cursive and connected letters, which most plate recognition systems are unable to recognize. We propose a technique using stroke extraction and analysis to recognize these nonstandard characters. The proposed technique first extracts the contour of the character and then the stroke direction which are used for classification. The advantage of this method is that the system requires no training. From the experiments performed, the method has a correct recognition accuracy of 95% and even works with standard car plates.

关键词： Pixel Character recognition Classification algorithms Licenses Artificial neural networks Training Algorithm design and analysis

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Multibody Motion Segmentation Using the Geometry of 6 Points in 2D images

Multibody Motion Segmentation Using the Geometry of 6 Points...

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International Conference on Pattern Recognition

作者： Klas Nordberg Vasileios Zografos Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9781424475421

We propose a method for segmenting an arbitrary number of moving objects using the geometry of 6 points in 2D images to infer motion consistency. This geometry allows us to determine whether or not observations of 6 points over several frames are consistent with a rigid 3D motion. The matching between observations of the 6 points and an estimated model of their configuration in 3D space is quantified in terms of a geometric error derived from distances between the points and 6 corresponding lines in the image. This leads to a simple motion inconsistency score that is derived from the geometric errors of 6 points, that in the ideal case should be zero when the motion of the points can be explained by a rigid 3D motion. Initial clusters are determined in the spatial domain and merged in motion trajectory domain based on the score. Each point is then assigned to a cluster by assigning the point to the segment of the lowest score. Our algorithm has been tested with real image sequences from the Hopkins155 database with very good results, competing with the state of the art methods, particularly for degenerate motion sequences. In contrast the motion segmentation methods based on multi-body factorization, that assumes an affine camera model, the proposed method allows the mapping from the 3D space to the 2D image to be fully projective.

关键词： Three dimensional displays Motion segmentation computer vision Trajectory Geometry Clustering algorithms image segmentation

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Moving shadow detection based on normalized eigenvalue of Wishart matrix

Moving shadow detection based on normalized eigenvalue of Wi...

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IEEE International Conference on Mechatronics and Automation

作者： Wei Zhang Q.M. Jonathan Wu Computer Vision and Sensing Systems Laboratory (CVSSL) Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

Moving objects detection is one basic problem in computer vision and various methods have been proposed. However, the performance of these methods may be deteriorated by the moving shadows. In this paper we present a novel method for the moving shadow detection. Based on the analysis of the illumination model, we prove that the normalized eigenvalue of image block is illumination invariant. The distribution of the normalized eigenvalue is discussed and a significance test is performed to classify each image pixel into foreground object or moving shadow. Experimental results on typical scenes show that the proposed method can detect moving shadows correctly. Quantitative comparison with the state-of-the-art demonstrates that the proposed method provides much improved results.

关键词： Eigenvalues and eigenfunctions Lighting Pixel Vehicles Covariance matrix Laboratories image color analysis

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Head pose estimation from images using Canonical Correlation analysis

Head pose estimation from images using Canonical Correlation...

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Applied imagery Pattern Recognition Workshop (AIPR)

作者： Jacob Foytik Vijayan K. Asari Menatoallah Youssef R. Cortland Tompkins Computer Vision and Wide Area Surveillance Laboratory Department of Electrical and Computer Engineering University of Dayton Dayton OH USA

Head pose estimation, though a trivial task for the human visual system, remains a challenging problem for computer vision systems. The task requires identifying the modes of image variance that directly pertain to pose changes, while generalizing across face identity and mitigating other image variances. Conventional methods such as Principal Component analysis (PCA) fail to identify the true relationship between the observed space and the pose variable, while supervised methods such as Linear Discriminant analysis (LDA) neglect the continuous nature of pose variation and take a discrete multi-class approach. We present a method for estimating head pose using Canonical Correlation analysis (CCA), where pose variation is regarded as a continuous variable and is represented by a manifold in feature space. The proposed technique directly identifies the underlying dimension that maximizes correlation between the observed image and pose variable. It is shown to increase estimation accuracy and provide a more compact image representation that better captures pose features. Additionally, an enhanced version of the system is proposed that utilizes Gabor filters for providing pose sensitive input to the correlation based system. The preprocessed input serves to increase the overall accuracy of the pose estimation system. The accuracy of the techniques is evaluated using the Pointing '04 and CUbiC FacePix(30) pose varying face databases and is shown to produce a lower estimation error when compared to both PCA and LDA based methods.

关键词： Face Estimation Feature extraction Principal component analysis Correlation Databases

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Road Network Extraction Using Edge Detection and Spatial Voting

Road Network Extraction Using Edge Detection and Spatial Vot...

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International Conference on Pattern Recognition

作者： Beril Sirmacek Cem Unsalan Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul Turkey

ISBN: (纸本)9781424475421

Road network detection from very high resolution satellite images is important for two main reasons. First, the detection result can be used in automated map making. Second, the detected network can be used in trajectory planning for unmanned aerial vehicles. Although an expert can label road pixels in a given satellite image, this operation is prone to errors. Therefore, an automated system is needed to detect the road network in a given satellite image in a robust manner. In this study, we propose a novel approach to detect the road network from a given panchromatic Ikonos satellite image. Our method has five main steps. First, we apply a nonlinear bilateral filtering to smooth the given image. Then, we extract Canny edges and the gradient information as local features. Using these local features, we generate a spatial voting matrix. This voting matrix indicates the possible locations of the road network pixels. By processing this voting matrix in an iterative manner, we detect initial road pixels. Finally, we apply a tracking algorithm on the voting matrix to detect the missing road pixels. We tested our method on various satellite images and provided the extracted road networks in the experiments section.

关键词： Roads image segmentation Satellites Feature extraction Pixel image edge detection image resolution

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Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm

Brain tissue segmentation using an unsupervised clustering t...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Milad Azarbad Ataollah Ebrahimzadeh Abbas Babajani-Feremi Faculty of Electrical and Computer Engineering BABOL University of Technology Babol Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit USA

image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional genetic algorithms. The proposed technique is based on the participle swarm optimization (PSO) and, in fact, is an unsupervised clustering method based on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not need to be known in advance. We evaluate and validate performance of the proposed method using simulation studies. The simulation results show that the accuracy of the proposed method is about 96%.

关键词： image segmentation Heating computers

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