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A fast image registration algorithm for Digital Subtraction Angiography

A fast image registration algorithm for Digital Subtraction ...

引用

Iranian Conference of Biomedical engineering (ICBME)

作者： Mansour Nejati Rassoul Amirfattahi Saeed Sadri Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Digital Subtraction Angiography (DSA) is a powerful technique for the visualization of blood vessels. The major problem encountered in DSA images is the presence of motion artifacts which arise from the misalignment of successive images in the sequence. In this paper, a fast registration algorithm for coronary angiographic images is proposed. It involves a feature-based selection of control points for which the displacement is computed by means of template matching, and the complete displacement vector field is constructed by means of interpolation. To reduce the computational cost of the template matching procedure, an iterative hill-climbing approach is used for optimization and for handling the local misalignments, the image warping is performed based on multilevel B-spline interpolation.

关键词： Image edge detection Artificial neural networks Interpolation Pixel

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Automatic segmentation of pallet images using the 2-D wavelet transform and YUV color space

Automatic segmentation of pallet images using the 2-D wavele...

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Iranian Conference on electrical engineering (ICEE)

作者： Aida Fooladivanda Narges Chehrerazi Saeed Sadri Rasoul Amirfattahi Mohammad Ali Montazeri Digital signal processing research Laboratory Department of Electrical and computer engineering Isfahan University of Technology Isfahan Iran

ISBN: (纸本)9781424467600

Segmentation is an important stage in automatic digital image processing. A special case of segmentation is to segment objects from their background. Among different segmentation algorithm for object detection, learning based approach is widely applied. In steel industry, pallets are moving on a rail. They have high resolution details in their structure and the image of a pallet taken by a camera in real time suffers from severe noise and illumination variations. The purpose of this paper is to segment the pallet from a frame of a sequence of video images, such that the pallet is segmented without degradation of resolution. We use the pallet image in YUV color space together with wavelet transform (WT) for detection. For classification Support Vector Machine (SVM) is incorporated to the images. It is shown that the above procedure segments the pallets successfully without degradation of resolution.

关键词： Image segmentation Wavelet transforms Image resolution Degradation Support vector machines Support vector machine classification Digital images Object detection Metals industry Rails

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Frame Interpolation Method Based on Adaptive Threshold and Adjacent Pixels

Frame Interpolation Method Based on Adaptive Threshold and A...

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

作者： Hyeongchul Oh Joohyun Lee Changki Min Jechang Jeong Image Communication and Signal Processing Laboratory Department of Electrical and Computer Engineering Hanyang University Seoul South Korea

This paper presents a frame interpolation algorithm using the motion information of some object within the estimated blocks. The proposed algorithm uses two initial frames that are generated based on the backward and forward frames using the motion vectors. In this process, the occlusion regions are occurred in two initial frames since the optimal block is only selected by threshold. Furthermore, the regions are irregularly appeared because of that the selected block is interpolated on half position between the estimated motion vectors. Two initial frames are merged to interpolate the occlusion regions. Also, the occlusion areas, which are still existence in the merged frame, are interpolated by using the neighboring pixel information and the available data in the occlusion block. In this paper, the experimental results show that the performance of the proposed algorithm has better PSNR and the visual quality than the conventional algorithms.

关键词： Pixel PSNR Interpolation Visualization Motion compensation Algorithm design and analysis Motion estimation

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Motion Compensated Frame Interpolation Using Adaptive Adjacency Pixel Information

Motion Compensated Frame Interpolation Using Adaptive Adjace...

引用

International Conference on computer Graphics, Imaging and Visualization, CGIV

We propose an adaptive motion compensated frame interpolation scheme which is capable of developing a frame rate from a lower number into a higher number and a decoded video quality at the decoder. The proposed algorithm utilizes the preliminary frames of the forward and backward directions using bilateral prediction. In the processing of the preliminary frames, an additional interpolation is performed to fill the occlusion areas. If the calculated values between the reference and current frames are larger than the predetermined threshold, the block is chosen as the occlusion area. In addition, we performed a re-search in order to acquire the best blocks, taking into account the number of available adjacent blocks for the occlusion areas. The experimental results show that the performance of the proposed algorithm has better PSNR and visual quality than those of the conventional methods.

关键词： Interpolation Pixel Robustness PSNR Motion estimation Algorithm design and analysis Motion compensation

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Online signature verification using combination of two classifiers

Online signature verification using combination of two class...

引用

Iranian Machine Vision and Image processing (MVIP)

作者： M. Saeidi R. Amirfattahi A. Amini M. Sajadi Digital Signal processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

The objective of signature verification is to distinguish forgery signature from genuine one. Online signature is the one which is registered through electronic devices such as digitizers and stored on computers in time sequence form. In this kind of signatures in addition to location information, time information such as speed and acceleration is stored. In this paper after accomplishment of some pre-processing procedures like normalization of signature size, smoothing and elimination of rotation on signatures using algorithms based on extremum matching of signals and ant colony, their time duration will be equalized. Afterwards, similarities between signatures will be determined using extended regression and finally will try to distinguish between forgery signatures from genuine one using support vector machine (SVM). The suggested online verification system is tested on SVC2004 signature set which is related to the first international signature verification competition and results are compared to respective results of participants. The results state that suggested method exhibits equal error rate (EER) of %4.3 in skilled forger group.

关键词： Forgery Mathematical model Equations Support vector machines Training Error analysis

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Adapting a MIMO/phased-array radar transmit beampattern to target location

Adapting a MIMO/phased-array radar transmit beampattern to t...

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International Workshop on Cognitive Information processing

作者： Fuhrmann, Daniel R. Browning, J. Paul Rangaswamy, Muralidhar Department of Electrical and Computer Engineering Michigan Technological University Houghton MI 49931 United States U.S. Air Force Research Laboratory Sensors Directorate Radar Signal Processing Branch Wright-Patterson AFB OH United States

ISBN: (纸本)9781424464593

Proposed MIMO and hybrid MIMO/phased array (HMPAR) radar systems have the potential for tremendous flexibility in the choice of the transmit beampattern, through the selection of multiple transmitted signals. This paper considers how one might exploit that flexibility in light of prior information or uncertainty in target spatial location, for parameter estimation or tracking applications. We first consider an idealized problem of distributing energy across multiple target sites given a prior probability distribution on those sites. It shown that the optimal allocation of energy would be proportional to the square root of the prior probability. Second, we propose a method to approximate this optimal distribution of energy using a MIMO radar or HMPAR radar system. The method chooses signals that realize an intrapulse beamscan over the region of interest, with a nonuniform distribution of scan points matched to the desired distribution of energy. © 2010 IEEE.

关键词： MIMO radar

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Improved learning in grid-to-grid neural network via clustering

Improved learning in grid-to-grid neural network via cluster...

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International Joint Conference on Neural Networks (IJCNN)

作者： W. White K. Iftekharuddin A. Bouzerdoum Intelligent Systems and Image Processing Laboratory Department of Electrical and Computer Engineering University of Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong NSW Australia

The maze traversal problem involves finding the shortest distance to the goal from any position in a maze. Such maze solving problems have been an interesting challenge in computational intelligence. Previous work has shown that grid-to-grid neural networks such as the cellular simultaneous recurrent neural network (CSRN) can effectively solve simple maze traversing problems better than other iterative algorithms such as the feedforward multi layer perceptron (MLP). In this work, we investigate improved learning for the CSRN maze solving problem by exploiting relevant information about the maze. We cluster parts of the maze using relevant state information and show an improvement in learning performance. We also study the effect of the number of clusters on the learning rate for the maze solving problem. Furthermore, we investigate a few code optimization techniques to improve the run time efficiency. The outcome of this research may have direct implication in rapid search and recovery, disaster planning and autonomous navigation among others.

关键词： Euclidean distance Kalman filters Training Navigation Artificial neural networks Testing

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Techniques for automatic magnetic resonance image classification

Techniques for automatic magnetic resonance image classifica...

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International Conference on Genetic and Evolutionary Computing

作者： Chen, Hsian-Min Chen, Shih-Yu Chai, Jyh Wen Chen, Clayton Chi-Chang Wu, Chao-Cheng Ouyang, Yen-Chieh Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions. © 2010 IEEE.

关键词： Support vector machines

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Fusion of visible and synthesised near infrared information for face authentication

Fusion of visible and synthesised near infrared information ...

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IEEE International Conference on Image processing

作者： Seyed Mohammad Mavadati Mohammad Taghi Sadeghi Josef Kittler Signal Processing Research Laboratory Department of Electrical & Computer Engineering Yazd University Iran CVSSP University of Surrey Guildford Surrey UK

Changes in illumination conditions can cause drastic variations in face appearance and affect the performance of a face authentication system. Near infrared (NIR) face imaging systems have been proposed as a promising way towards illumination invariant face verification. We show that when NIR face images cannot be observed, learning the relationship between NIR information and the corresponding visible images can provide useful complementary information about visible light image data. In particular, we use Canonical Correlation Analysis (CCA) to synthesise the NIR eigenfaces from their corresponding visible ones. In this paper, the verification performance of a CCA-based synthesising algorithm is developed first. Although, synthesised NIR images do not perform as well as the real NIR, it is shown that by fusing the visible and synthesised near infrared information at the score level, the performance of the authentication system considerably improves.

关键词： Face Lighting Correlation Support vector machines Authentication Databases Linear regression

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Volume-based magnetic resonance brain image classification

Volume-based magnetic resonance brain image classification

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International Conference on Genetic and Evolutionary Computing

作者： Chiou, Yaw-Jiunn Chai, Jyh-Wen Chen, Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Ouyang, Yen-Chieh Su, Wu-Chung Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. © 2010 IEEE.

关键词： Support vector machines

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