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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是1521-1530 订阅

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3D models coding and morphing for efficient video compression

3D models coding and morphing for efficient video compressio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： F. Galpin R. Balter L. Morin K. Deguchi Graduate School of Information Sciences University of Tohoku Sendai Japan IRISA INRIA-University of Rennes Rennes France Research and Development France Telecom Cesson-Sevigne France

this paper presents new results in the field of video coding and compression using 3D information. Contrary to prior art in 3D model-based coding where 3D models have to be known, the 3D models are here automatically computed from the original video sequence. the camera parameters and the scene content are supposed unknown and we only assume a static scene viewed by a moving monocular camera. the video sequence is then processed on the fly. A stream of 3D models is extracted and compressed, using adapted computer vision and compression techniques. the visualization of the reconstructed video sequence uses an adapted morphing method. We finally show results obtained with the proposed compression scheme, compared with the H26L compression standard. We demonstrate the efficiency of our approach, especially in the field of very low bitrate coding.

关键词： Video compression Video sequences Cameras Layout Video coding Art Streaming media computer vision Visualization Bit rate

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Estimation, smoothing, and characterization of apparent diffusion coefficient profiles from High Angular Resolution DWI

Estimation, smoothing, and characterization of apparent diff...

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Chen W. Guo Q. Zeng X. Yan F. Huang H. Zhang G. He B.C. Vemuri Y. Liu Department of Mathematics University of Florida USA Department of Psychiatry and Neuroscience University of Florida USA Department of Computer and Information Science and Engineering University of Florida USA

We present a new variational framework for recovery of apparent diffusion coefficient (ADC)from High Angular Resolution Diffusion-weighted (HARD) MRI. the model approximates the ADC profiles by a 4th order spherical harmonic series (SHS), whose coefficients are obtained by solving a constrained minimization problem. By minimizing the energy functional, the ADC profiles are estimated and regularized simultaneously across the entire volume. In this model, feature preserving smoothing is achieved by minimizing a non-standard growth functional, and the estimation is based on the original Stejskal-Tanner equation. the antipodal symmetry and positiveness of the ADC are also accommodated into the model. Furthermore, coefficients of the SHS and the variance of ADC profiles from its mean are used to characterize the diffusion anisotropy. the effectiveness of the proposed model is depicted via application to both simulated and HARD MRI human brain data. the characterization of non-Gaussian diffusion based on the proposed model showed consistency with known neuroanatomy.

关键词： Smoothing methods Magnetic resonance imaging Anisotropic magnetoresistance Equations Brain modeling Q measurement Performance evaluation Diffusion tensor imaging Helium Mathematics

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Classification of Lung Data by Sampling and Support Vector Machine

Classification of Lung Data by Sampling and Support Vector M...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： J. Dehmeshki J. Chen M.V. Casique M. Karakoy MedicSight Public Limited Company London UK

Developing a computer-Assisted Detection (CAD) system for automatic detection of pulmonary nodules in thoracic CT is a highly challenging research area in the medical domain. It requires the application of state-of-the-art image processing and pattern recognition technologies. the object recognition and feature extraction phase of such a system generates a large number of data set. As there is normally a large quantity of non-nodule objects within this data set while the nodule objects are sparse, a Gaussian mixture model-based sampling method is used to reduce the non-nodule data and thus the classification complexity. the support vector machine, a classifier motivated from the statistical learning theory, is used in the pattern recognition stage of automatic pulmonary nodule detection. After the training process, only support vectors will be used in the classification process. As the support vector machine classifier gives the unique optimal solution, the experiment on the lung nodule data shows a fast and satisfactory classification rate.

关键词： Lungs Sampling methods Support vector machines Support vector machine classification pattern recognition Image sampling Computed tomography Biomedical imaging Application software Image processing

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vision rehabilitation with the optic nerve visual prosthesis

Vision rehabilitation with the optic nerve visual prosthesis

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： C. Veraart F. Duret M. Brelen J. Delbeke Neural Rehabilitation Engineering Laboratory Universite catholique de Louvain Brussels Belgium

A blind volunteer with retinitis pigmentosa has been chronically implanted with an optic nerve visual prosthesis. the axons of retinal ganglion cells have been successfully activated by electrical stimuli to evoke many distinct phosphenes over a large portion of the visual field. No sensations other than visual have been elicited. Controlled electrical stimulation of the optic nerve proved able to convey visual information useful for the localization and discrimination of objects.

关键词： Visual prosthesis Biomedical optical imaging Optical sensors Stimulated emission Retina Pigmentation Electrical stimulation pattern recognition Optical control Blindness

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Transitional appearance-based motion tracking for real-time breast self-examination supervision

Transitional appearance-based motion tracking for real-time ...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Yuqin Hu R.N.G. Naguib A.G. Todman S.A. Amin H. Al-Omishy BIOCORE School of Mathematical and Information Sciences Coventry University Coventry UK Walsgrave Hospital University Hospitals Coventry and Warwickshire NHS Trust Coventry UK

Despite much research and clinical trials, breast cancer still presents a serious threat of premature death to women. Early detection of the lumps in the breast is a key contributing factor to the successful treatment of this potentially fatal disease. Performing breast self-examination (BSE) in an accurate manner can assist a woman in detecting any abnormalities in her breasts, which may mark the onset of potential disease. this is also an essential tool used to enhance breast awareness. Using the hand, in a specific configuration, and palpating the entire breast in a certain movement pattern can optimise her feeling of the breast, In this paper we describe an intelligent automated algorithm for tracking the finger pads of a moving hand with the movement videos captured by a common web camera. the algorithm employs the principle of HCRA (hand configuration recognition algorithm) and its refinement through a simple but novel transitional appearance-based model. A novel hand motion recognition algorithm (HMRA) is developed to recognise the motion pattern. Desirable tracking and recognition results have been achieved and the robustness of this algorithm is demonstrated in this paper.

关键词： Tracking Breast cancer Motion analysis pattern recognition Diseases Feedback Machine vision History Cancer detection Hospitals

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Continuous classifier training for myoelectrically controlled prostheses

Continuous classifier training for myoelectrically controlle...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： A.W. Plumb A.D.C. Chan A.R. Goge Department of Systems and Computer Engineering Carleton University ONT Canada

Myoelectrically controlled prostheses use pattern recognition systems to classify input motions. Typically, these systems are initially trained offline using a set of training data. Changing conditions cause an increase in signal variation, leading to higher error rates. For better adaptability, a continuously trained classifier was developed. Data with valid class decisions are used to retrain the classifier with the class decisions used as classification targets. In this implementation the classifier validates decisions by using a retraining buffer to locate consecutive, identical majority vote decisions. Retraining is performed by incorporating new valid feature vectors, selected from the retraining buffer, into the training set, while discarding older vectors. Using the continuously trained classifier, an average improvement of 2.57% was seen over the noncontinuously trained classifier.

关键词： Prosthetics Electrodes pattern recognition Linear discriminant analysis Training data Wrist Motion control Automatic control Testing Vectors

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ECGSIM: an Interactive Tool for the Study of the Relation between the Electric Activity of the Heart and the QRST Waveforms at the Body Surface

ECGSIM: an Interactive Tool for the Study of the Relation be...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： T.F. Oostendorp A. van Oosterom Department of Medical Physics and Biophysics Radboud University Nijmegen Netherlands Department of Cardiology University of Lausanne Lausanne Switzerland

ECGSIM is an interactive computer program that solves the forward problem of electrocardiology. the user may set the depolarization and repolarization times at the heart surface, as well as the transmembrane potential amplitude. the program computes the resulting ECGs at the body surface. In this way, the effect of changes of the electric activity of the heart on the ECG can be studied. ECGSIM may be downloaded, free of charge, from the web.

关键词： Heart Surface waves Electrocardiography Inverse problems Myocardium Electronic switching systems pattern recognition Conductivity Conductors thorax

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Hand motion segmentation against skin colour background in breast awareness applications

Hand motion segmentation against skin colour background in b...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Yuqin Hu R.N.G. Naguib A.G. Todman S.A. Amin H. Al-Omishy A. Oikonomou N. Tucker BIOCORE School of Mathematical and Information Sciences Coventry University Coventry UK Walsgrave Hospital University Hospitals Coventry and Warwickshire NHS Trust Coventry UK Warwick Manufacturing Group University of Warwick Coventry UK

Skin colour modelling and classification play significant roles in face and hand detection, recognition and tracking. A hand is an essential tool used in breast self-examination, which needs to be detected and analysed during the process of breast palpation. However, the background of a woman's moving hand is her breast that has the same or similar colour as the hand. Additionally, colour images recorded by a web camera are strongly affected by the lighting or brightness conditions. Hence, it is a challenging task to segment and track the hand against the breast without utilising any artificial markers, such as coloured nail polish. In this paper, a two-dimensional Gaussian skin colour model is employed in a particular way to identify a breast but not a hand. First, an input image is transformed to YCbCr colour space, which is less sensitive to the lighting conditions and more tolerant of skin tone. the breast, thus detected by the Gaussian skin model, is used as the baseline or framework for the hand motion. Secondly, motion cues are used to segment the hand motion against the detected baseline. Desired segmentation results have been achieved and the robustness of this algorithm is demonstrated in this paper.

关键词： Motion segmentation computer vision Skin Breast Face detection Image segmentation Motion detection Face recognition Cameras Brightness

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Optimized Gaussian mixture models for upper limb motion classification

Optimized Gaussian mixture models for upper limb motion clas...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Y. Huang K.B. Englehart B. Hudgins A.D.C. Chan Department of Electrical and Computer Engineering University of New Brunswick NB Canada University of New Brunswick Fredericton NB CA Inst. of Biomed. Eng. New Brunswick Univ. Fredericton NB Canada Department of Systems and Computer Engineering Carleton University ONT Canada

this work introduces the use of Gaussian mixture models (GMM) for discriminating multiple classes of limb motions using continuous myoelectric signals (MES). the purpose of this work is to investigate an optimum configuration of a GMM-based limb motion classification scheme. For this effort, a complete experimental evaluation of the Gaussian mixture motion model is conducted on a 12-subject database. the experiments examine algorithmic issues of the GMM including the model order selection and variance limiting. the final classification performance of this GMM system has been compared with that of three other classifiers (a linear discriminant analysis (LDA), a linear perceptron neural network (LP) and a multilayer perceptron (MLP) neural network) . the Gaussian mixture motion model attains 96.3% classification accuracy using four channel MES for distinguishing six limb motions and is shown to outperform the other motion modeling techniques on an identical six limb motion task.

关键词： pattern recognition Error analysis Prosthetics Motion control Linear discriminant analysis Biomedical engineering Niobium Electromyography Skin Electrodes

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Novel approach for spastic hemiplegia classification through the use of support vector machines

Novel approach for spastic hemiplegia classification through...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： A.J. Salazar O.C. De Castro R.J. Bravo Department of Electronics and Circuits Universidad Simón Bolívar Caracas Venezuela Department of Industrial Technology Universidad Simón Bolívar Caracas Venezuela

the use of computer based classification methods for spastic hemiplegia (SH) is a relatively new approach. Before and still, the classification is primarily done by specialized physicians through manual template matching on kinematics plots of the sagittal plane data from the affected limb generated by gait laboratories. In the past, several methods for computer based automation of this classification have been attempted. this paper introduces the use of support vector machine (SVM) as a model that contributes to this process. the results obtained from the use of SVM are quite efficient considering the data set utilized involves patients from an array of ages and both sex. It should be mentioned that even though the number of patients is not trivial; the percentages of accurate classification represented are promising. Since the SVM method improves its accuracy with an increased number of training cases, this approach has the advantage of becoming more accurate with time. Many more significant discoveries are expect from the introduction of SVM in the analysis of SH and further studies of gait related pathologies are expected to contribute as well.

关键词： Support vector machines Support vector machine classification pattern recognition Kinematics Space technology Pathology Machine learning Kernel Polynomials Multi-layer neural network

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