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IEEE International Symposium on Biomedical Imaging

作者： Ernst Schwartz Roman Gottardi Johannes Holfeld Christian Loewe Martin Czerny Georg Langs Computational Image Analysis and Radiology Lab (CIR) Medical University of Vienna Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Division of Cardiothoracic Surgery Department of Surgery Medical University of Vienna Austria Division of Interventional Radiology Department of Radiology Medical University of Vienna Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

ISBN: (纸本)9781424441259;9781424441266

Cardiovascular interventions in the region of the aortic isthmus such as stent-grafting and vessel transposition introduce substantial changes in the deformation properties of the affected vessels. The changes play a fundamental role in the long-term prognosis for any such treatment, but are only poorly understood to date. We explore a fully automated method to quantify the deformation patterns of the thoracic aorta in gated computed tomography sequences. The aorta is segmented by a level set approach that accurately identifies the vessel lumen in each frame of the sequence. Consequently, landmarks on the vessel wall in each frame are registered using a probabilistic method. This allows for the measurement of global and local deformation properties. We evaluate our method on synthetic datasets and report first results of its application on real world data.

关键词： Biomedical imaging image segmentation Radiology Cardiology Surgery Computed tomography Level set Electrocardiography Pathology Deformable models

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3D FACE REPRESENTATION AND RECOGNITION BY INTRINSIC SHAPE DESCRIPTION MAPS

3D FACE REPRESENTATION AND RECOGNITION BY INTRINSIC SHAPE DE...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zhe Guo Yanning Zhang Yong Xia Zenggang Lin Dagan Feng Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP) School of Computer Science Northwestern Polytechnical University Xi'an China Biomedical and Multimedia Information Technology (BMIT) Research Group School of Information Technologies University of Sydney Australia

ISBN: (纸本)9781424442959

We present a novel method for 3D face recognition, in which the 3D facial surface is first mapped into a 2D domain with specified resolution through a global optimization by constrained conformal geometric maps. The Intrinsic Shape Description Map (ISDM) is then constructed through a modeling technique capable to express geometric and appearance information of the 3D face. Hence the 3D surface matching problem can be simplified to a 2D image matching problem, which greatly reduces the computational complexity. Finally, the Intrinsic Shape Description Feature (ISDF) of ISDM and the discrimination analysis can be calculated. Experimental results implemented on GavabDB demonstrate that our proposed method significantly outperforms the existing methods with respect to pose variation.

关键词： Face recognition Conformal Geometric Maps Intrinsic Shape Description Map

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Study of blind image restoration algorithm based on non-negative independent component analysis

Study of blind image restoration algorithm based on non-nega...

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Li, Sanfeng Wang, Shijie Luo, Limin Image Science and Technology Lab. Southeast University Nanjing 210096 China

ISBN: (纸本)9780819478061

Blind image restoration is a great part of image processing, which refers to the technology field that study how to restore the original image from the observed image without sufficient prior knowledge about the degradation process. Recently, as a novel method for blind source separation based on high statistics, Independent Component Analysis (ICA)'s computation algorithms and applications are widely studied. In this paper, we present a Non-negative ICA algorithm which is used to the blind image restoration with the constraint of non-negativity. Besides this, through a matrix of de-correlation, it can reduce the requirements to the independence of the source signals in ICA algorithm. Through some computer simulation and experiments for real images, we show the effectiveness of the presented method. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Independent component analysis

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A new non-homogeneous Markov random field model based on fuzzy membership for brain MRI segmentation

A new non-homogeneous Markov random field model based on fuz...

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MIPPR 2009 - Medical Imaging, Parallel Processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Xu, Rong Luo, Limin Image Science and Technology Lab. Southeast University Nanjing 210096 China

ISBN: (纸本)9780819478085

In this paper, we propose a new non-homogeneous Markov random field model based on fuzzy membership to resolve over-segmentation caused by traditional MRF model in the application of Brain MRI segmentation. Herein, we use fuzzy membership to estimate the parameters in the model. Simulated brain MRIs with the noise of different intensity and real brain MRIs are utilized in experiments. The results illustrate our method effectively reduces over-segmentation and improves final segmentation results and precision, and its performance is more powerful than that of kernel-based fuzzy c-means clustering algorithm and the traditional MRF model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image segmentation

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Design of a wearable rehabilitation robotic hand actuated by pneumatic artificial muscles

Design of a wearable rehabilitation robotic hand actuated by...

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2009 7th Asian Control Conference, ASCC 2009

作者： Kexin, Xing Jian, Huang Qi, Xu Yongji, Wang Key Lab. of Image Processing and Intelligent Control Department of Control Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9788995605691

Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and the rehabilitation specialist can improve the hand motor performance of the patient survived from stroke or traumatic brain injury. However, the therapy is time-consuming and expensive. Furthermore, it is difficult to evaluate the result of therapy quantitatively and objectively. The goal of this research is to develop a novel wearable device for robotic assistive hand repetitive therapy. We designed a pneumatic artificial muscle (PAM) driven rehabilitation robotic hand that is wearable and provides assistive forces required for grasping and release movements. The robot has two distinct degrees of freedom at the thumb and the fingers. The embedded sensors are employed to obtain the feedback position and force information for robot control and quantitative evaluation of task performance. To encourage the patients' self-regulation and improve the performance of the task, a PC based Virtual Reality game was developed. In order to realize a compliant and smooth motion, cubic spline functions are adopted in the trajectory planning of metacarpophalangeal joints. To realize the trajectory tracking control, a conventional PID controller was implemented and evaluated. Experiments were performed to confirm the effectiveness of the proposed device and methods. The robot has the potential of providing supplemental at-home therapy in addition to in the clinic treatment. ©2009 ACA.

关键词： Degrees of freedom (mechanics)

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Modeling of cortical signals using echo state networks

Modeling of cortical signals using echo state networks

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Zhou, Hanying Wang, Yongji Huang, Jiangshuai Key Lab. for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology China

ISBN: (纸本)9780819478078

Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Brain

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A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution

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Journal of Zhejiang University-science A(Applied Physics & Engineering) 2009年第12期10卷 1705-1713页

作者： Wen CHEN Xiang-zhong FANG Yan CHENG Shanghai Key Lab of Digital Media Processing and Transmissions Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai 200240 China College of Information Technology East China University of Political Science and Law Shanghai 201620 China

This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.

关键词： Edge preservation Multidimensional Lorentzian distribution （MDL） Super resolution Threshold

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Epidemic spreading with variant infection rates on scale-free network

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6th International Symposium on Neural Networks, ISNN 2009

作者： Hong, Liu Ouyang, Min Mao, Zijun Chen, Xueguang Institute of System Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)3642015123

In this paper, we proposed a susceptible-infected model with variant infection rates because different individuals have different resistance to diseases in different periods of real epidemic events. We consider two cases: Case 1, we know every individual's infection rate to a kind of epidemic, satisfy a type of distribution. Case 2, assume all individuals have same initial infection rates, a susceptible individual's infection rate will be less than the initial rate if he is not infected after limited number of contacts with infected ones. For both two cases, at the time tD, preventive and control measures bring into effects, every individual's infection rate would decrease. We implemented this models on scale-free networks, and found that the epidemic process before the time tD in Case 1 is almost the same as that in Standard SI model if the infection rate in Standard SI model equals the mean infection rate in Case 1. Furthermore, using numerical simulation, we analysis the effects of the parameter tD, and find the bimodal distribution of final infection rates. Finally, we conclude what we get in this paper and give our future direction. © 2009 Springer Berlin Heidelberg.

关键词： Complex networks

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Interdependency analysis of infrastructures

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6th International Symposium on Neural Networks, ISNN 2009

作者： Mao, Zijun Hong, Liu Fei, Qi Ouyang, Ming Institute of System Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)3642015123

Electric power, potable water, telecommunications, natural gas, and transportation are examples of critical infrastructures, the intrinsic feature of which are suitable for network analysis. This paper proposes a method framework based on the complex network to explore the interdependency of these infrastructures. We describe the topological characterization of two interdependent small-sized real networks, based on which the interdependent model is devised. We define the interdependent factor to characterize the direct interdependency of two infrastructures, and also its fluctuation in the situations of random removal and targeted removal on the electric and water infrastructures is studied. Furthermore we propose a matrix M to reflect all direct and indirect interactions among infrastructures, which also makes contributions to the understanding of infrastructures catastrophes. © 2009 Springer Berlin Heidelberg.

关键词： Complex networks

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Architecture of behavior-based and robotics self-optimizing memory controller

Architecture of behavior-based and robotics self-optimizing ...

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2009 IEEE International Conference on Robotics and Automation, ICRA '09

作者： Osman, Hassab Elgawi Image Science and Engineering Lab. Tokyo Institute of Technology School of Engineering 4259 Nagatsuta Midori-ku Yokohama 226-8503 Japan

ISBN: (纸本)9781424427895

In this paper we represent a preliminary research on designing a behavior-based adaptive system utilizing self-optimizing memory controller. Rather than holistic search for the whole memory contents the model adopt associated feature analysis to successively memorize a newly experience state-action pair as an action of past experience, produce motor commands that make the controlled system to behave desirably in the future. Actor-Critic learning is used to adaptively tuning the control parameters, while an on-line variant of random forests (RF) learner is used to approximate the policy of Actor and the value function of Critic. Learning capability of the proposed model is experimentally examined through a task of Cart-Pole balancing problem, designed in mind as computation with perception. The result shows that the robot with self-optimizing memory acquired behaviors such as balancing the pole, displays planning based on past experiences. © 2009 IEEE.

关键词： Poles

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