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Journal ArticleDOI

Uncalibrated view synthesis using planar segmentation of images

TL;DR: Experimental comparisons with the images synthesized using the actual three -dimensional scene structure and camera poses show that the proposed method effectively describes scene changes by viewpoint movements without estimation of 3 -D and camera information.
Abstract: This paper presents an uncalibrated v iew synthesis method using piecewise planar regions that are extracted from a given set of image pairsthrough planar segmentation. Our work concentrates on a view synthesis method that does not needestimation of camera parameters and scene structure. Forour goal, we simply assume that images of real world are composed of piecewise planar regions. Then, we perform view synthesis simply with planar regions and homographiesbetween them. Here, for accurate extraction of planar homographies and piecewise pla nar regions in images, the proposed method employs iterative homography estimation and color segmentation -based planar region extraction. The proposed method synthesizes the virtual view image using a set of planar regions as well as a set of corresponding homographies. Experimental comparisons with the images synthesized using the actual three -dimensional (3-D) scene structure and camera poses show that the proposed method effectively describes scene changes by viewpoint movements without estimation of 3 -D and camera information.
References
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Journal ArticleDOI
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

46,906 citations


"Uncalibrated view synthesis using p..." refers methods in this paper

  • ...35 The scale invariant feature transform (SIFT) [28] is applied to two test image pairs to establish image correspondences....

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Book
01 Jan 2000
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Abstract: From the Publisher: A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. The book covers the geometric principles and how to represent objects algebraically so they can be computed and applied. The authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly.

15,558 citations

Journal ArticleDOI
ZhenQiu Zhang1
TL;DR: A flexible technique to easily calibrate a camera that only requires the camera to observe a planar pattern shown at a few (at least two) different orientations is proposed and advances 3D computer vision one more step from laboratory environments to real world use.
Abstract: We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.

13,200 citations

Journal ArticleDOI
TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

11,727 citations


"Uncalibrated view synthesis using p..." refers methods in this paper

  • ...Before segment-based matching, we apply a mean-shift segmentation [29] for color segmentation to split the target image into a number of homogeneous color segments....

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Journal ArticleDOI
TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
Abstract: Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.

7,458 citations


"Uncalibrated view synthesis using p..." refers methods in this paper

  • ...With a rectified image pair, uncalibrated view synthesis [13] was presented, in which a stereo algorithm estimates the horizontal displacement, called disparity, between two images....

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  • ...Given more than two images, depth estimation is performed mostly using stereo algorithms [13] or triangulation [14]....

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  • ...Besides camera calibration, depth estimation [11–14] is needed to synthesize a virtual view regardless of calibrated/uncalibrated view synthesis methods....

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