Camera calibration is to estimate the intrinsic and extrinsic parameters of a camera. Most of object-based calibration methods used 3D or 2D pattern. A novel and more flexible 1D object-based calibration was introduced only a couple of years ago, but merely for estimation of intrinsic parameters. The estimation of extrinsic papers is essential when multiple cameras are involved for simultaneously taking images from different view angles and when the knowledge of relative locations between the cameras is required. Though it is relatively simple using 2D or 3D calibration pattern, the estimation of extrinsic parameters is not obvious using 1D pattern. In this paper, we will perform a 1D camera calibration involving both intrinsic and extrinsic parameters.

This paper presents a practical method that estimates illumination distribution from shadows where the shadows are assumed to be cast on a textured, Lambertian surface. Previous methods usually require that the reflectance property of the surface be constant or uniform, or need an additional image to cancel out the effects of varying albedo of the textured surface. We deal with an estimation problem for which surface albedo information is not available. In this case, the estimation problem corresponds to an underdetermined one. We show that combination of regularization by correlation and some user-specified information can be a practical method for solving the problem. In addition, as an optimization tool for solving the problem, we develop a constrained nonnegative quadratic programming (NNQP) technique into which not only regularization but also user-specified information are easily incorporated. We test and validate our method on both synthetic and real images and present some experimental results.

With a monocular view, the nonrigid recovery of 3D motion and time-varying shapes of a deforming object may be impossible without any prior information, because ambiguous, multiple solutions exist for motion and shapes which produce the same projection image. In this paper, as a preceding step to the nonrigid recovery of a deforming object, we develop an approach for estimating the approximate average shape and motion of the object. This reasonably solves the ambiguity problem in nonrigid recovery. By investigating the internal structures of nonrigid objects, we introduce a novel concept, called DoN (Degree of Nonrigidity). Based on this, we propose an iterative certainty reweighted factorization method. In addition, we refine and improve the method by reformulating it in a robust manner to cope with outliers existing in the tracked features. Finally, we present some experimental results on both synthetic data and a real video sequence.

In this paper, we propose a method for generating realistic shadows of virtual objects inserted into a real video sequence. Our aim is to improve and extend the work of Sato, Sato, and Ikeuchi, (1999), which is based on a static camera, to the case of a video sequence. This extension consists of several procedures: calibration of a moving video camera and a graphic camera, removing false shadows occurring due to a shortcoming of the static camera approach for the estimation of an illumination distribution, and so on. The calibration of the moving camera is solved by camera self-calibration and, with it, we designed a flexible graphic world coordinate system embedding technique called "match move". We also show that the shortcoming of the previous static camera approach is overcome by using information from video sequence. Finally we present the experimental results of a real video sequence.

In this paper, we propose a method for locating 3D position of a soccer ball from monocular image sequence of soccer games. Toward this goal, we adopted ground-model-to-image transformation together with physics-based approach, that a ball follows the parabolic trajectory in the air. By using the transformation the heights of a ball can be easily calculated using simple triangular geometric relations given the start and the end position of the ball on the ground. Here the heights of a ball are determined in terms of a player's height. Even if the end position of a ball is not given on the ground due to kicking or heading of a falling ball before it touches the ground, the most probable trajectory can be determined by searching based on the physical fact that the ball follows a parabolic trajectory in the air. We have tested and experimented with a real image sequence the results of which seem promising.