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We describe an Augmented Reality system using the corners of a color cube for camera calibration. In the augmented image the cube is replaced by a computer generated virtual object.
The cube is localized in an image by the CSC color segmentation algorithm. The camera projection matrix is estimated with a linear method that is followed by a nonlinear refinement step.
Because of possible missclassifications of the segmented color regions and the minimum number of point correspondences used for calibration, the estimated pose of the cube may be very erroneous for some frames; therefore we perform outlier detection and treatment for rendering the virtual object in an acceptable manner.
This work presents a technique for computing dense disparity maps from a binocular stereo camera system. The methods are applied in an Augmented Reality setting for combining real and virtual worlds with proper occlusions. The proposed stereo correspondence technique is based oil area matching and facilitates an efficient strategy by using the concept of a three-dimensional similarity accumulator whereby occlusions are detected and object boundaries are extracted correctly. The main contribution of this paper is the way we fill the accumulator using absolute differences of images and computing a mean filter on these difference images. This. is. where the main advantages of the accumulator approach can be exploited, since all entries can be computed in parallel and thus extremely efficient. Additionally, we-perform an asymmetric correction step and a post-processing of the disparity maps that maintains object edges.
In this paper we address the problem of using quaternions in unconstrained nonlinear optimization of 3-D rotations. Quaternions representing rotations have four elements but only three degrees of freedom, since they must be of norm one.
This constraint has to be taken into account when applying e.g. the Levenberg-Marquardt algorithm, a method for unconstrained nonlinear optimization widely used in computer vision. We propose an easy to use method for achieving this.
Experiments using our parametrization in photo grammetric bündle -adjustment are presented at the end of the paper.
This paper presents new vector quantization based methods for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements.
Data selection can improve the accuracy of classic hand-eye calibration, and make it possible in the first place in situations where the standard approach of manually selecting positions is inconvenient or even impossible, especially when using continuously recorded data.
A variety of methods is proposed, which differ from each other in the dimensionality of the vector quantization compared to the degrees of freedom of the rotation representation, and how the rotation angle is incorporated.
The performance of the proposed vector quantization based data selection methods is evaluated using data obtained from a manually moved optical tracking system (hand) and an endoscopic camera (eye).