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检索条件"任意字段=1966 IEEE Computer Society Conference on Computer Vision and Pattern Recognition"

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Characterizing fMRI Activations within Regions of Interest (ROIs) Using 3D Moment Invariants

Characterizing fMRI Activations within Regions of Interest (...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： B. Ng R. Abugharbieh Xuemei Huang M.J. McKeown Department of Electrical and Computer Engineering Pacific Parkinson's Research Center University of British Columbia Vancouver BC Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada Department of Neurology School of Medicine University of North Carolina Chapel Hill NC USA Department of Medicine (Neurology) Pacific Parkinson's Research Center University of British Columbia Vancouver BC Canada

A novel method is proposed for characterizing spatial distribution changes in functional magnetic resonance imaging (fMRI) activation statistics under different experimental conditions. The proposed technique, based on three dimensional (3D) invariant moment descriptors, was applied to fMRI data recorded from eight healthy subjects performing internally or externally-cued finger tapping sequences. Voxel-based activation statistics were characterized in several regions of interest (ROIs), including the supplementary motor area (SMA), cerebellum, primary motor cortex, prefrontal cortex, and caudate. Examining the activation patterns of these neural regions using 3D moment invariants revealed that the patterns of activation regions were significantly different during externally and internally cued tasks when computed across subjects. In contrast, traditional methods that are based on amplitude of the activation statistics demonstrated reduced discriminability. The results suggest that the spatial distribution of activation is a more sensitive measure of activation changes, and complements conventional fMRI analyses.

关键词： Magnetic heads Filtering Low pass filters Nonlinear filters Linear discriminant analysis Biomedical imaging Nervous system Fingers Coils Smoothing methods

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Specifying, Interpreting and Detecting High-level, Spatio-Temporal Composite Events in Single and Multi-Camera Systems

Specifying, Interpreting and Detecting High-level, Spatio-Te...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： S. Velipasalar L.M. Brown A. Hampapur Electrical Engineering Department Princeton University Princeton NJ USA IBM Thomas J. Watson Research Center Hawthorne NY USA

Detecting and tracking moving objects are important and challenging problems which have attracted much attention from the research community. However, in most cases, it is not enough to only track the objects. The goal should be to detect the occurrences of events of interest, which is important for applications such as video surveillance, video browsing and indexing. Yet, event detection introduces the challenge of providing the flexibility to specify customized events with varying complexity, and entering them to a system in a generic way. The event definitions should not be pre-defined and hard-coded. We introduce a spatio-temporal event detection system which lets the users to specify multiple composite events of high-complexity, and then detects their occurrence automatically. Events can be defined on a single camera view or across multiple camera views. Semantically higher level event scenarios can be built by using the building blocks, which we call the primitive events, and combining them by operators. More importantly, the newly defined composite events can be combined with each other. This layered structure makes the definition of events with higher and higher complexity possible. The event definitions are written to an XML file, which is then parsed and communicated to the tracking engines running on the videos of the corresponding cameras. With the proposed system, we have reached the next level and managed to go from detecting "a person exiting the building" to detecting "a person coming from the south corridor of the building and then exiting the building".

关键词： Event detection Object detection Cameras Video surveillance computer languages Indexing XML Engines Statistics Monitoring

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Euclidean Structure from Confocal Conics: Theory and Application to Camera Calibration

Euclidean Structure from Confocal Conics: Theory and Applica...

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conference on computer vision and pattern recognition (CVPR)

作者： P. Gurdjos Jun-Sik Kim In-So Kweon UPS IRIT-TCI Toulouse France Department of EECS KAIST Daejeon South Korea

ISBN: (纸本)0769525970

Plane-based calibration is now a very popular procedure because of its flexibility. One key step consists in detecting a set of coplanar features, from which the Euclidean structure of the corresponding 3D plane has to be computed. We suggest to use confocal conics as calibration targets, as they offer undeniable advantages over other ones (e.g., points or lines) in terms of detection and estimation, especially in the presence of partial occlusion. We introduce important projective and Euclidean properties of the linear family of conics (i.e., the confocal conic range), spanned by two confocal conics. In particular, we rely on the fact that the circular point-envelope - a rank-2 conic that encodes the 2D Euclidean structure - is a degenerate member of any confocal conic range. This allows us to give closed-form solutions in three cases: one conic with known foci, two confocal conics with known product of ratios of semi axes, and two unknown confocal conics. The performances of the proposed algorithms (consisting of a few lines of Matlab-like code) show up high accuracies for both intrinsic and extrinsic camera parameters. In addition to experiments with synthetic data, a video sequence is processed, showing off the interest of using confocal conics as calibration targets, for augmented reality purposes.

关键词： Cameras Calibration Closed-form solution pattern matching computer vision Uninterruptible power systems computer languages Video sequences Augmented reality Solid modeling

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Multiple Cardiac Arrhythmia recognition Using Adaptive Wavelet Network

Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavel...

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

作者： Chia-Hung Lin Pei-Jarn Chen Yung-Fu Chen You-Yun Lee Tainsong Chen Department of Electrical Engineering Kao-Yuan Institute of Technology Kaohsiung Taiwan Institute of Biomedical Engineering National Cheng Kung University Tainan Taiwan Department of Electrical Engineering Southern Taiwan University of Technology Tainan Taiwan Department of Computer Science and Information Engineering Da-Yeh University Changhua Taiwan

This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN). Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method

关键词： Adaptive systems Electrocardiography Heart beat Feature extraction pattern recognition computer vision Neural networks Wavelet analysis Data mining Performance analysis

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Image Registration in Intra-oral Radiography

Image Registration in Intra-oral Radiography

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

作者： C.C. Leung K.L. Yiu W.K. Tsui K.Y. Zee Department of Electrical & Electronic Engineering University of Hong Kong Hong Kong China Faculty of Dentistry University of Hong Kong Hong Kong China

Image registration is one of the image processing methods which is widely used in computer vision, pattern recognition, and medical imaging. In digital subtraction radiography, image registration is one of the important prerequisites to match the reference and subsequent images. In this paper, we propose an automatic non-rigid registration method namely curvature-based registration that relies on a curvature based penalizing term and its application on dental radiography. The regularizing term of this intensity-based registration approach provides affine linear transformation so that pre-registration step is no longer necessary. This leads to faster and more reliable solutions. The implementation of this approach is based on the numerical solution of the underlying Euler-Lagrange equations. In addition, a comparison between this algorithm and linear alignment method (LAM) with 20 image pairs is presented

关键词： Image registration Diagnostic radiography Dentistry Biomedical imaging Equations Application software Bone diseases Image processing computer vision Medical diagnostic imaging

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Information-Theoretic Feature Detection in Ultrasound Images

Information-Theoretic Feature Detection in Ultrasound Images

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

作者： Greg Slabaugh Gozde Unal Ti-Chiun Chang Siemens Corporate Research Inc. Princeton NJ USA

The detection of image features is an essential component of medical image processing, and has wide-ranging applications including adaptive filtering, segmentation, and registration. In this paper, we present an information-theoretic approach to feature detection in ultrasound images. Ultrasound images are corrupted by speckle noise, which is a disruptive random pattern that obscures the features of interest. Using theoretical probability density functions of the speckle intensity distributions, we derive analytic expressions that measure the distance between distributions taken from different regions in an ultrasound image and use these distances to detect features. We compare the technique to classic gradient-based feature detection methods

关键词： computer vision Ultrasonic imaging Speckle Biomedical image processing Adaptive filters Image segmentation Probability density function Image analysis Density measurement Ultrasonic variables measurement

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition: CVPR 2004 - Volume I

Proceedings of the 2004 IEEE Computer Society Conference on ...

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004

The proceedings contains 120 papers from the 2004 ieee computer society conference on computer vision And pattern recognition: CVPR 2004 - Volume I. The topics discussed include: restoration of curved document images through 3D shape modeling;fast wide baseline matching for visual hull;shedding light on stereoscopic segmentation;window-based, discontinuity preserving stereo;space-time video completion and tracking loose-limbed people.

关键词： computer vision

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004 - Volume 2

Proceedings of the 2004 IEEE Computer Society Conference on ...

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004

The proceedings contains 138 papers from the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004-Volume 2. The topics discussed include: feature-centric evaluation for efficient cascaded object detection;learning methods for generic object recognition and invariance to pose and lighting;searching the web with mobile images for location recognition;controlled modulation imaging;automatic cascade training with perturbation bias;tracking multiple humans in crowded environment;and bayesian face recognition using support vector machine and face clustering.

关键词： Object recognition

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ieee computer society conference on computer vision and pattern recognition Workshops

IEEE Computer Society Conference on Computer Vision and Patt...

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2004 ieee computer society conference on computer vision and pattern recognition Workshops, CVPRW 2004

The proceedings contain 186 papers. The topics discussed include: towards recovery of 3D chromosome structure;object-based visual 3D tracking of articulated objects via kinematic sets;non-rigid motion estimation and spatio-temporal realignment in FMRI;stochastic meta-descent for tracking articulated structures;gender recognition from walking movements using adaptive three-mode PCA;differential structure in non-linear image embedding functions;semantic-level understanding of human actions and interactions using event hierarchy;fusion of a multiple hypotheses color model and deformable contours for figure ground segmentation in dynamic environments;outlier rejection in deformable model tracking;spline-based motion recovery for 3D surfaces using nonrigid shape properties;and constraints for heterogeneous sensor auto-calibration.

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computer vision for music identification: Video demonstration

Computer vision for music identification: Video demonstratio...

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conference on computer vision and pattern recognition

作者： Ke, Y Hoiem, D Sukthankar, R Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

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