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International Conference on image and Graphics (ICIG)

作者： Liu Yang Baolong Guo Wei Ni Institute of Intelligent Control and Image Engineering Xidian University Xi'an China

A novel multifocus image fusion algorithm based on contourlet transform and region statistics was proposed. For good properties of multiscale, localization, directionality and anisotropy, the multifocus source images are first decomposed using contourlet transform. Fusion operations based on region statistics are implemented for all subbands in each scale and direction. Regional variance and local energy are utilized as the fusion rule in low frequency and high frequency subbands, respectively. Experimental results show that the proposed method can extract image features more effectively and obtain better fusion performance. Compared with traditional gradient pyramid algorithm and wavelet based fusion algorithm, the mean square error of the proposed algorithm is increased by 34.8%and 42.6%.

关键词： image fusion Statistics Frequency Signal processing algorithms image edge detection Anisotropic magnetoresistance Layout Spatial resolution image resolution Signal resolution

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Example based Super-Resolution Algorithm of Video in Contourlet Domain

Example based Super-Resolution Algorithm of Video in Contour...

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International Conference on image and Graphics (ICIG)

作者： Wei Ni Baolong Guo Liu Yang Institute of Intelligent Control and Image Engineering Xidian University Xi'an China

An example based super-resolution algorithm for digital video using contourlet transform is proposed in this paper. The input is a low resolution video sequence together with a high resolution still image of similar content. Firstly, the low resolution frames are interpolated to the same spatial resolution as the reference still image. For the good properties of directional, multiscale and anisotropy, the nonsub-sampled contourlet is utilized to create the training set of transform coefficient patches from the high resolution still image. Block based motion estimation is then applied inside the complete training set to find the best matching between interpolated frame and reference still image. According to the correspondence between low frequency and high frequency pairs, the missing high frequency information of the input frame can be easily learned from the training set. Finally, an inverse contourlet transform is applied to the interpolated frame and supplement high frequency subbands to recover the super-resolved image. Preliminary experimental results on video frames show that the proposed super- resolution algorithm outperforms conventional spatial interpolation methods and wavelet based interpolation algorithm both in visual quality and the PSNR value.

关键词： Spatial resolution image resolution Signal resolution Filter bank Frequency image reconstruction Band pass filters Videoconference Video sequences Anisotropic magnetoresistance

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IHS and support value integrated approach to improve pansharpening quality of QuickBird image

IHS and support value integrated approach to improve panshar...

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MIPPR 2007: Multispectral image Processing

作者： Zheng, Sheng Yang, Changeai Liu, Jian Tian, Jinwen Electronics and Information Engineering Department Huazhong University of Science and Technology Wuhan 430074 China Institute of Intelligent Vision and Image Information Best College of Electrical Engineering and Information Technology China Three Gorges University Yichang 443002 China State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819469519

image fusion is an important tool in remote sensing, since many Earth observation satellites provide both high-resolution panchromatic (Pan) and low-resolution multispectral (MS) images. To date, many image fusion techniques have been developed. However, the available algorithms can hardly produce a satisfactory fusion result for IKONOS and QuickBird images. Among the existing fusion algorithms, the IHS technique is the most widely used one, and the wavelet fusion is the most frequently discussed one in recent publications because of its advantages over other fusion techniques. But the color distortion of these two techniques is often obvious. The support value fusion technique demonstrates some advantages over the conventional methods. This study presents a new fusion approach that integrates the advantages of both the IHS and the support value techniques to reduce the color distortion of QuickBird fusion results. Different QuickBird images have been fused with this new approach. Visual and statistical analyses prove that the concept of the proposed extended fast IHS (eFIHS) and support value integration is promising, and it does significantly improve the fusion quality compared to conventional IHS (eFIHS) and wavelet fusion techniques.

关键词： image fusion

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Boosting discriminative model for moving cast shadow detection

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Jisuanji Xuebao/Chinese Journal of Computers 2007年第8期30卷 1295-1301页

作者： Zha, Yu-Fei Chu, Ying Wang, Xun Ma, Shi-Ping Bi, Du-Yan Signal and Information Processing Laboratory Engineering College Air Force Engineering University Xi'an 710038 China Key Laboratory for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative model to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction;secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property;thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately.

关键词： image processing

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

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Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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FDTD model of photonic crystal laser with four-energy-level gain media

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Jisuan Wuli/Chinese Journal of Computational Physics 2007年第4期24卷 452-456页

作者： Wang, Hong Ouyang, Zhengbiao Han, Yanling Meng, Qingsheng Center of THz Technology Shenzhen University Shenzhen 518060 China College of Engineering and Technology Shenzhen University Shenzhen 518060 China School of Physical Electronics University of Electronic Science and Technology of China Chengdu 610054 China Mathematics and Physics Department China University of Geosciences Wuhan 430074 China State Education Commission Laboratory for Image Processing and Intelligent Control Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

A model of one-dimensional photonic crystal laser with four-energy-level gain media is presented. The model consists of Maxwell-Bloch equations coupled with rate equations of electronic population in a photonic crystal laser system. The discretization of coupled equations is accomplished with finite different time domain (FDTD). Spectra, lasing mode, lasing threshold and dynamics of the photonic crystal laser are obtained. The simulation shows good agreement with theoretical predictions.

关键词： Photonic crystals

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NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL image SEGMENTATION

NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAG...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhoundi-Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

ISBN: (纸本)9781424406715;1424406714

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.

关键词： Biomedical imaging image segmentation Shape Couplings Brain Magnetic resonance Magnetic resonance imaging Principal component analysis Probability density function Iterative algorithms

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Speckle reduction algorithm for SAR images using contourlet transform

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Journal of Information and Computational Science 2006年第1期3卷 83-94页

作者： Ni, Wei Guo, Baolong Yang, Liu Institute of Intelligent Control and Image Engineering Xidian University Xi'an 710071 China

In this paper, a novel speckle reduction algorithm for SAR images based on contourlet transform was proposed. Logarithmic transform is first performed to convert the original multiplicative speckle noise into additive noise. For noise can be effectively separated from real image signal in contourlet transform domain, a contourlet based hard thresholding algorithm is then applied. The Monte-Carlo method is adopted to estimate the statistics of contourlet coefficients for speckle noise, thus determining the optimal threshold set. To further improve the visual quality of despeckling, we also utilize the cycle-spinning technique. Experimental results show that the proposed contourlet based algorithm outperforms conventional algorithms in terms of both speckle reduction and edge preservation.

关键词： Synthetic aperture radar

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image mosaic technique based on interest points feature matching

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Guangdianzi Jiguang/Journal of Optoelectronics Laser 2006年第6期17卷 733-737页

作者： Wu, Jian-Ning Guo, Bao-Long Feng, Zong-Zhe Institute of Intelligent Control and Image Engineering Xidian University Xi'an 710071 China

An automatic image mosaic technique based on interest points feature matching (IPFM) was proposed. Firstly the combined corner and edge detector is used to gain the interest points, and a kind of bidirectional greatest correlative coefficient match law is used to extract the initial feature points pair, then the false feature points pair is rejected by space coordinate distance criterion, finally the correct match feature points pair is used to realize image stitching;In addition, the field value concept is introduced in the image fusion technology to solve the smooth transition problem through overlap region. The experimental results indicated that this method can fast and accurately extract the corresponding feature points between two images and reduce the probability greatly which matches by mistake. Under the existing slight rotator situation, this method still could effectively realize the image smoothly stitching.

关键词： Imaging techniques

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Spatially Adaptive Kernels for Adaptive Spatial Filtering of fMRI Data

Spatially Adaptive Kernels for Adaptive Spatial Filtering of...

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International Joint Conference on Neural Networks (IJCNN)

作者： V. Taimouri G.-A. Hossein-Zadeh H. Soltanian-Zadeh Control and Intelligent Processing Centre of Excellence ECE Department Faculty of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics and Medical Image Analysis Laboratory Henry Ford Health System Detroit MI USA

Making use of neighborhood time series is an effective way of noise reduction in fMRI data. However the conventional averaging methods blur activated areas. In this paper, a filter with adaptive kernel is designed such that its kernel size and direction are defined at each voxel. First, for finding the optimum size of kernel, a linear combination of some isotropic Gaussian filters with different variances is used, and optimum value of variance is specified. Then, the appropriate kernel direction is determined by a linear combination of some anisotropic basic filters with various directions. The weights of these linear combinations can be calculated by using the restricted canonical correlation method. The proposed method is compared with a similar method based on the steerable filters and the results show that the proposed method improves the ROC curve and prohibits false spread of the activation areas.

关键词： Kernel Adaptive filters Filtering Nonlinear filters Anisotropic magnetoresistance Magnetic separation Process control intelligent control Correlation Magnetic resonance imaging

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