Camera Calibration Results Research Articles

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

Camera model and its calibration are required in many applications for coordinate conversions between the two-dimensional image and the real three-dimensional world. Self-calibration method is usually chosen for camera calibration in uncontrolled environments because the scene geometry could be unknown. However when no reliable feature correspondences can be established or when the camera is static in relation to the majority of the scene, self-calibration method fails to work. On the other hand, object-based calibration methods are more reliable than self-calibration methods due to the existence of the object with known geometry. However, most object-based calibration methods are unable to work in uncontrolled environments because they require the geometric knowledge on calibration objects. Though in the past few years the simplest geometry required for a calibration object has been reduced to a 1D object with at least three points, it is still not easy to find such an object in an uncontrolled environment, not to mention the additional metric/motion requirement in the existing methods. Meanwhile, it is very easy to find a 1D object with two end points in most scenes. Thus, it would be very worthwhile to investigate an object-based method based on such a simple object so that it would still be possible to calibrate a camera when both self-calibration and existing object-based calibration fail to work. We propose a new camera calibration method which requires only an object with two end points, the simplest geometry that can be extracted from many real-life objects. Through observations of such a 1D object at different positions/orientations on a plane which is fixed in relation to the camera, both intrinsic (focal length) and extrinsic (rotation angles and translations) camera parameters can be calibrated using the proposed method. The proposed method has been tested on simulated data and real data from both controlled and uncontrolled environments, including situations where no explicit 1D calibration objects are available, e.g. from a human walking sequence. Very accurate camera calibration results have been achieved using the proposed method.

Canonical Representation and Multi-View Geometry of Cylinders

This paper first introduces a canonical representation for cylinders. The canonical representation introduced here is closely related to the Plucker line representation. In this paper, we show that this representation is an appropriate one for computer vision applications. In particular, it allows us to easily develop a series of mathematical methods for pose estimation, 3D reconstruction, and motion estimation. One of the major novelties in this paper is the introduction of the main equations dominating the three view geometry of cylinders. We show the relationship between cylinders' three-view geometry and that of lines (Spetsakis and Aloimonos, 1990; Weng et al., 1993) and points (Shashua, 1995) defined by the trilinear tensor (Hartley, 1997), and propose a linear method, which uses the correspondences between six cylinders across three views in order to recover the motion and structure. Cylindrical pipes and containers are the main components in the majority of chemical, water treatment and power plants, oil platforms, refineries and many other industrial installations. We have developed a professional software, called CyliCon, which allows efficient as-built reconstruction of such installations from a series of pre-calibrated images. Markers are used for this pre-calibration process. The theoretical and practical results in this paper represent the first steps towards marker-less calibration and reconstruction of such industrial sites. Here, the experimental results take advantage of the two-view and three-view geometry of cylinders introduced in this paper to provide initial camera calibration results.

The presentation of the results of aerial camera calibration

The basic concepts involved in the calibration of aerial cameras are discussed. A number of different ways in which the results of calibrations may be presented and methods for transforming one form of presentation into another are given. Tables are included which facilitate the work, as well as a tabular method for locating a point of symmetry.

The presentation of the results of aerial camera calibration

The basic concepts involved in the calibration of aerial cameras are discussed. A number of different ways in which the results of calibrations may be presented and methods for transforming one form of presentation into another are given. Tables are included which facilitate the work, as well as a tabular method for locating a point of symmetry.