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View generation for three-dimensional scenes from video sequences

IEEE TRANSACTIONS ON image processing 1997年第4期6卷 584-598页

作者： Chang, NL Zakhor, A Video and Image Processing Laboratory Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA

This paper focuses on the representation and view generation of three-dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence, Furthermore, instead of using multiple calibrated stationary cameras or range scanners, we derive our depth maps from image sequences captured by an uncalibrated camera with only approximately known motion, We propose an adaptive matching algorithm that assigns various confidence levels to different regions in the depth maps, Nonuniform bicubic spline interpolation is then used to fill in low confidence regions in the depth maps. Once the depth maps are computed at preselected viewpoints, the intensity and depth at these locations are used to reconstruct arbitrary views of the 3-D scene, Specifically, the depth maps are regarded as vertices of a deformable 2-D mesh, which are transformed in 3-D, projected to 2-D, and rendered to generate the desired view. Experimental results are presented to verify our approach.

关键词： Layout Video sequences Cameras image sequences image reconstruction Geometry Solid modeling Spline Interpolation Mesh generation

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Content-based colour image filters

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ELECTRONICS LETTERS 1997年第3期33卷 202-203页

作者： Plataniotis, KN Androutsos, D Venetsanopoulos, AN Department of Electrical and Computer Engineering Digital Signal/Image Processing Laboratory University of Toronto Toronto Canada

A nonlinear ranked-order filter based on a content model of similarity is proposed for colour image processing. Simulation results indicate that the new filter suppresses impulsive as well as Gaussian noise and preser... 详细信息

关键词： image processing colour

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1.5V resistive fuse for image smoothing and segmentation

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ELECTRONICS LETTERS 1997年第10期33卷 851-852页

作者： Athreya, JKV Gregson, PH Department of Electrical Engineering Computer Vision and Image Processing Laboratory Technical University of Nova Scotia Halifax Canada

A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage;it switches off if the differential voltage is higher than the set value. The useful operation range of the circuit is 0.4V with a supply voltage of 1.5V and threshold voltages of V-Tn = 0.828V and V-Tp = -0.56V for n and g channel MOSFETs, respectively.

关键词： image processing segmentation (image processing)

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ML parameter estimation and minimum distance classifier for texture analysis using wavelet transform

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computerS & INDUSTRIAL engineering 1997年第1-2期33卷 449-452页

作者： Singh, R Singh, R Vasquez, R Laboratory for Applied Remote Sensing and Image Processing Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez Campus PR 06681-5000 Puerto Rico

This work concentrates on comparing the performance of the minimum distance classifier and maximum-likelihood classifier for texture analysis. A tree-structured wavelet transform has been used for extracting the features and the comparison is based on the correct classification percentage. The results indicate that the maximum-likelihood classifier performs marginally better than the mahalanobis distance for some feature sets. The Euclidean distance did not prove to be powerful in distinguishing the textures. The performance of various orthogonal wavelet transforms have also been compared in order to find out the best wavelet for each of the classifiers considered. (C) 1997 Elsevier Science Ltd.

关键词： texture analysis ML parameter estimation minimum distance wavelet transform

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Two-stage neural network for volume segmentation of medical images

Two-stage neural network for volume segmentation of medical ...

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1997 IEEE International Conference on Neural Networks (ICNN 97)

作者： Ahmed, MN Farag, AA Computer Vision and Image Processing Laboratory University of Louisville Department of Electrical Engineering Louisville KY 40292 United States

ISBN: (纸本)0780341236

In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be apdated to real-time imaging scenarios.

关键词： segmentation pattern analysis learning in vision

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Temporal prediction of video sequences using an image warping technique based on color segmentation 9th

Temporal prediction of video sequences using an image warpin...

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9th International Conference on image Analysis and processing, ICIAP 1997

作者： Herodotou, N. Venetsanopoulos, A.N. Digital Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of Toronto 10 King's College Road TorontoONM5S 3G4 Canada

ISBN: (纸本)3540635076

An image warping technique based on segmented regions is introduced for the temporal prediction of videophone-type sequences. At the encoder, a set of control points are determined from the previous frame and their corresponding best matched points are determined from the current frame. The selection process of these points is achieved by segmenting the previous frame into different regions using a color segmentation technique based on a recursive histogramming approach and the control points are subsequently chosen along the region boundaries. The spatial offset of these points between the previous and current frame are represented as motion vectors. The facial area is intraframe encoded to avoid distortions of the facial features which convey critical information. At the decoder, the same segmentation and control point selection algorithm is used along with the motion vectors in order to find the region boundaries of the predicted frame. The facial area is decoded and an affine transformation is finally used to determine the remaining regions and form the predicted frame. This technique produces results that are free from blocking artifacts as in the conventional block matching method, with only a moderate increase in computational complexity. © Springer-Verlag Berlin Heidelberg 1997.

关键词： Decoding

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3D reconstruction of the human jaw from a sequence of images

3D reconstruction of the human jaw from a sequence of images

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Ahmed S. Yamany E. Hemayed S. Ahmed S. Roberts A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.

关键词： image reconstruction Humans computer vision Data acquisition Cameras Shape Data mining Genetic algorithms Implants Machine vision

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Two-stage neural network for volume segmentation of medical images

Two-stage neural network for volume segmentation of medical ...

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International Conference on Neural Networks

作者： M.N. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be adapted to real-time imaging scenarios.

关键词： Neural networks image segmentation Biomedical imaging Feature extraction Principal component analysis Computed tomography Magnetic resonance imaging Brain Labeling Clustering algorithms

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Model-based detection and correction of corrupted wavelet coefficients

Model-based detection and correction of corrupted wavelet co...

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IEEE International Conference on image processing

作者： N. Strobel S.K. Mitra B.S. Manjunath Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Santa Barbara USA University of California Santa Barbara Santa Barbara CA US

image decomposition based on the discrete wavelet transform (DWT) has been proposed for efficient storage and progressive transmission of images for visual browsing in digital image libraries. Although the compression aspects of the DWT have been carefully researched, reconstruction errors due to corrupted wavelet coefficients have received less attention. In this paper we consider the problem of bit errors affecting uniformly quantized wavelet coefficients. The proposed method, which is based on a local image model, simultaneously detects and masks corrupted wavelet coefficients.

关键词： Wavelet coefficients image coding image reconstruction Discrete wavelet transforms Filters Decoding Error correction image edge detection Signal processing image processing

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The contrast pyramid based image code

The contrast pyramid based image code

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IEEE International Conference on image processing

作者： Tian-Hu Yu Jianhua Chen S.K. Mitra Department of Information Engineering Chinese University of Hong Kong New Territories Hong Kong China Department of Electrical and Computer Engineering Signal and Image Processing Laboratory University of California Santa Barbara CA USA

In this paper, a contrast pyramid based image coding method is described in which a simple nonlinear difference operation is introduced to generate residual information between two contiguous level images. The representation of the residual information can be considered as a local contrast measure. As the difference image in established pyramid coding techniques, the contrast image has low variance, and data compression is achieved by quantizing the contrast image. Based on Weber's law and spatial masking of the human visual system, further data compression can be attained by selecting only perceptually significant information from the contrast image and coding it efficiently.

关键词： image coding Laplace equations Low pass filters Data compression Humans Data structures Nonlinear filters Filtering image generation Visual system

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