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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Example-based style synthesis

Example-based style synthesis

引用

conference on computer vision and pattern recognition (cvpr)

作者： I. Drori D. Cohen-Or H. Yeshurun School of Computer Science Tel-Aviv University Tel-Aviv Israel

We introduce an example-based synthesis technique that extrapolates novel styles for a given input image. The technique is based on separating the style and content of image fragments. Given an image with a new style and content, it is first adaptively partitioned into fragments. Stitching together novel fragments produces a coherent image in a new style for a given content. The aggregate of synthesized fragments approximates a globally non-linear model with a set of locally linear models. We show the result of our method for various artistic, sketch, and texture filters and painterly styles applied to different image content classes.

关键词： computer vision computer science Aggregates Filters Painting Rendering (computer graphics) Table lookup Pixel computer society pattern recognition

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Extraction and integration of window in a 3D building model from ground view images

Extraction and integration of window in a 3D building model ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Sung Chun Lee R. Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

Details of the building facades are needed for high quality fly-through visualization or simulation applications. Windows form a key structure in the detailed facade reconstruction. In this paper, given calibrated facade texture (i.e. the rectified texture), we extract and reconstruct the 3D window structure of the building. We automatically extract windows (rectangles in the rectified image) using a profile projection method, which exploits the regularity of the vertical and horizontal window placement. We classify the extracted windows using 2D dimensions and image texture information. The depth of the extracted windows is automatically computed using window classification information and image line features. A single ground view image is enough to compute 3D depths of the facade windows in our approach.

关键词： Windows Buildings Image reconstruction Data mining Intelligent robots Visualization computer vision Focusing Cameras Solid modeling

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Depth from Familiar Objects: A Hierarchical Model for 3D Scenes

Depth from Familiar Objects: A Hierarchical Model for 3D Sce...

引用

conference on computer vision and pattern recognition (cvpr)

作者： E.B. Sudderth A. Torralba W.T. Freeman A.S. Willsky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology UK

We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D location and appearance of visual features. Uncertainty in the number of object instances depicted in a particular image is then achieved via a transformed Dirichlet process. In contrast with image-based approaches to object recognition, we model scale variations as the perspective projection of objects in different 3D poses. To calibrate the underlying geometry, we incorporate binocular stereo images into the training process. A robust likelihood model accounts for outliers in matched stereo features, allowing effective learning of 3D object structure from partial 2D segmentations. Applied to a dataset of office scenes, our model detects objects at multiple scales via a coarse reconstruction of the corresponding 3D geometry.

关键词： Layout Geometry Solid modeling Object recognition Stereo vision Robustness Image segmentation Object detection Uncertainty Image reconstruction

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Two plane camera calibration: a unified model

Two plane camera calibration: a unified model

引用

conference on computer vision and pattern recognition (cvpr)

作者： G.-Q. Wei S.D. Ma National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Nat. Lab. of Pattern Recognition Chinese Acad. of Sci. Beijing China

A unified model for camera calibration is presented. The model does not assume any explicit form of imaging process and can perform linear back projection while considering all lens distortions. This model is well suited to stereo reconstruction. It has been shown that the original two-plane model, the linear and nonlinear pin hole model, can all be realized as special cases of this model. With the two-plane model, optimal orientation of the two reference planes can be found intuitively.< >

关键词： Cameras Calibration Interpolation Lenses Nonlinear distortion computer vision Spline Image storage pattern recognition Automation

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Nonsingular discriminant feature extraction for face recognition

Nonsingular discriminant feature extraction for face recogni...

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2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Liao, Chih-Pin Chien, Jen-Tzung Department of Computer Science and Information Engineering National Cheng Kung University Tainan 70101 Taiwan

ISBN: (纸本)0780388747

It is popular to extract discriminant features using Fisher linear discriminant analysis (LDA) for general pattern recognition. LDA aims to find an optimal discriminant transformation matrix, which maximizes the ratio of between-class scatter to within-class scatter. However, in case of small sample size and high dimensional data, LDA is prone to be unrealizable due to the singularity of scatter matrices. In this paper, we present a nonsingular transformation prior to performing LDA. This method is to transform general features using all eigenvectors of scatter matrix with nonzero eigenvalues. As a result, the scatter matrix of transformed features is nonsingular. Subsequently, the discriminant transformation is applied according to LDA using the new scatter matrices. The superiority of nonsingular discriminant analysis of between-class matrix comes from the shrinkage of within-class scatters and accordingly the enhancement of Fisher class separability. From the experiments on facial databases, we find that the nonsingular discriminant feature extraction achieves significant face recognition performance compared to other LDA-related methods for a wide range of sample sizes and class numbers. © 2005 ieee.

关键词： Feature extraction

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Reconstructing open surfaces from unorganized data points

Reconstructing open surfaces from unorganized data points

引用

conference on computer vision and pattern recognition (cvpr)

作者： J.E. Solem A. Heyden School of Technology and Society Malmö University Sweden

In this paper a method for fitting open surfaces to an unorganized set of data points is presented using a level set representation of the surface. This is done by tracking a curve, representing the boundary, on the implicitly defined surface. This curve is given as the intersection of the level set describing the surface and an auxiliary level set. These two level sets are propagated using the same motion vector field. Special care has to be taken in order for the surfaces not to intersect at other places than at the desired boundary. Methods for accomplishing this are presented and a novel fast scheme for finding good initial values is proposed. This novel method gives a piecewise linear approximation of the initial surface boundary using a partition of the convex hull. With the described method open surfaces can be fitted to point clouds obtained using structure from motion techniques. This paper solves an important practical problem since in many cases the surfaces in the scene are open or can only be viewed from certain directions. Successful experiments on several data sets support the method.

关键词： Surface reconstruction Surface fitting Level set Image reconstruction Layout Cameras Clouds computer vision Surface treatment Vectors

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The alignment of objects with smooth surfaces: error analysis of the curvature method

The alignment of objects with smooth surfaces: error analysi...

引用

conference on computer vision and pattern recognition (cvpr)

作者： R. Basri M. I. T. A. I. Laboratory Cambridge MA USA

The recognition of objects with smooth bounding surfaces from their contour images is addressed. In particular, the curvature method is applied to ellipsoidal objects and the error for different rotations of the objects is computed analytically. It is seen that the error depends on the exact shape of the ellipsoid (namely, the relative lengths of its axes), and it increases as the ellipsoid becomes elongated in the Z-direction. It is shown that the errors are usually small, and that, in general, a small number of models is required to predict the appearance of an ellipsoid from all possible views.< >

关键词： Error analysis Ellipsoids Shape Laboratories Predictive models Image recognition Object recognition Neuroscience Artificial intelligence Contracts

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Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics

Modeling Correspondences for Multi-Camera Tracking Using Non...

引用

conference on computer vision and pattern recognition (cvpr)

作者： V.I. Morariu O.I. Camps Computer Vision Laboratory University of Maryland College Park MD USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D trajectories and fully take advantage of the additional information available from the multiple sensors. Previous approaches to the "correspondence across views" problem include matching features, using camera calibration information, and computing homographies between views under the assumption that the world is planar. However, it can be difficult to match features across significantly different views. Furthermore, calibration information is not always available and planar world hypothesis can be too restrictive. In this paper, a new approach is presented for matching correspondences based on the use of nonlinear manifold learning and system dynamics identification. The proposed approach does not require similar views, calibration nor geometric assumptions of the 3D environment, and is robust to noise and occlusion. Experimental results demonstrate the use of this approach to generate and predict views in cases where identity labels become ambiguous.

关键词： Target tracking Cameras Calibration Nonlinear dynamical systems Manifolds computer vision Trajectory Sensor systems Noise robustness Laboratories

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Patch-based convolutional neural network for whole slide tissue image classification

Patch-based convolutional neural network for whole slide tis...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Hou, Le Samaras, Dimitris Kurc, Tahsin M. Gao, Yi Davis, James E. Saltz, Joel H. Dept. of Computer Science Stony Brook University United States Dept. of Biomedical Informatics Stony Brook University United States Dept. of Applied Mathematics and Statistics Stony Brook University United States Oak Ridge National Laboratory United States Dept. of Pathology Stony Brook Hospital United States Cancer Center Stony Brook Hospital United States

ISBN: (纸本)9781467388511

Convolutional Neural Networks (CNN) are state-of-theart models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.

关键词： Image classification

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Syntactic pattern classification by branch and bound search

Syntactic pattern classification by branch and bound search

引用

conference on computer vision and pattern recognition (cvpr)

作者： A.Y. Commike J.J. Hull Department of Computer Science State University of New York University at Buffalo Buffalo NY USA

A methodology for classifying syntactic patterns that performs a branch-and-bound search over a set of prototypes is proposed. The prototypes are first clustered hierarchically and the search is performed over the hierarchy. The proposed technique is applied to a pattern recognition system in which images are described by the sequence of features extracted from the chain codes of their contours. A rotationally invariant string distance measure is defined that compares two feature strings. The methodology discussed is compared to a nearest neighbor classifier that uses 12000 prototypes. The proposed technique decreases the time required to recognize a pattern by 93% and maintains a recognition rate of greater than 90%.< >

关键词： pattern classification pattern recognition Prototypes Nearest neighbor searches Character recognition Training data computer science Feature extraction Rotation measurement

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