About: Vanishing point is a research topic. Over the lifetime, 2216 publications have been published within this topic receiving 32127 citations.
Papers published on a yearly basis
TL;DR: An algebraic representation is developed which unifies the three types of measurement and permits a first order error propagation analysis to be performed, associating an uncertainty with each measurement.
Abstract: We describe how 3D affine measurements may be computed from a single perspective view of a scene given only minimal geometric information determined from the image This minimal information is typically the vanishing line of a reference plane, and a vanishing point for a direction not parallel to the plane It is shown that affine scene structure may then be determined from the image, without knowledge of the camera's internal calibration (eg focal length), nor of the explicit relation between camera and world (pose) In particular, we show how to (i) compute the distance between planes parallel to the reference plane (up to a common scale factor)s (ii) compute area and length ratios on any plane parallel to the reference planes (iii) determine the camera's location Simple geometric derivations are given for these results We also develop an algebraic representation which unifies the three types of measurement and, amongst other advantages, permits a first order error propagation analysis to be performed, associating an uncertainty with each measurement We demonstrate the technique for a variety of applications, including height measurements in forensic images and 3D graphical modelling from single images
TL;DR: Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.
Abstract: In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.
03 Aug 1997
TL;DR: A new method called TIP (Tour Into the Picture) is presented for easily making animations from one 2D picture or photograph of a scene using a graphical user interface, which is not intended to construct a precise 3D scene model.
Abstract: A new method called TIP (Tour Into the Picture) is presented for easily making animations from one 2D picture or photograph of a scene. In TIP, animation is created from the viewpoint of a camera which can be three-dimensionally "walked or flownthrough" the 2D picture or photograph. To make such animation, conventional computer vision techniques cannot be applied in the 3D modeling process for the scene, using only a single 2D image. Instead a spidery mesh is employed in our method to obtain a simple scene model from the 2D image of the scene using a graphical user interface. Animation is thus easily generated without the need of multiple 2D images. Unlike existing methods, our method is not intended to construct a precise 3D scene model. The scene model is rather simple, and not fully 3D-structured. The modeling process starts by specifying the vanishing point in the 2D image. The background in the scene model then consists of at most five rectangles, whereas hierarchical polygons are used as a model for each foreground object. Furthermore a virtual camera is moved around the 3D scene model, with the viewing angle being freely controlled. This process is easily and effectively performed using the spidery mesh interface. We have obtained a wide variety of animated scenes which demonstrate the efficiency of TIP. CR
23 Jun 1998
TL;DR: The novel contributions are that in a stratified context the various forms of providing metric information can be represented as circular constraints on the parameters of an affine transformation of the plane, providing a simple and uniform framework for integrating constraints.
Abstract: We describe the geometry constraints and algorithmic implementation for metric rectification of planes. The rectification allows metric properties, such as angles and length ratios, to be measured on the world plane from a perspective image. The novel contributions are: first, that in a stratified context the various forms of providing metric information, which include a known angle, two equal though unknown angles, and a known length ratio; can all be represented as circular constraints on the parameters of an affine transformation of the plane-this provides a simple and uniform framework for integrating constraints; second, direct rectification from right angles in the plane; third, it is shown that metric rectification enables calibration of the internal camera parameters; fourth, vanishing points are estimated using a Maximum Likelihood estimator; fifth, an algorithm for automatic rectification. Examples are given for a number of images, and applications demonstrated for texture map acquisition and metric measurements.
TL;DR: This paper decomposes the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point.
Abstract: Given a single image of an arbitrary road, that may not be well-paved, or have clearly delineated edges, or some a priori known color or texture distribution, is it possible for a computer to find this road? This paper addresses this question by decomposing the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based upon a local voting region using high-confidence voters, whose texture orientations are computed using Gabor filters, and a new vanishing-point-constrained edge detection technique for detecting road boundaries. The proposed method has been implemented, and experiments with 1003 general road images demonstrate that it is effective at detecting road regions in challenging conditions.
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