This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using non-linear search techniques to find the best distortion parameters that straighten these lines. Unlike other approaches, we also provide fast, closed-form solutions to the distortion coefficients. Experiments to evaluate the performance of this approach on synthetic and real data are reported.

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive a new distortion measure that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also show how to use this measure to find fast, closed-form solutions to the distortion coefficients. Some experiments to evaluate the performance of this approach on synthetic and real data are reported.

Camera systems with zoom lenses are inherently more useful than those with passive lenses due to their \nexibility and controllability. However, their calibration raises several challenges. In this paper, we present a neural framework for zoom-lens camera calibration that can capture complex variations in the camera model parameters across continuous ranges in the lens control space, while minimizing the calibration error over all the calibration data. To automate the tedious process of col-lecting calibration data, the calibration approach should be prepared to handle possible outliers in the data. We demonstrate how the cal-ibration approach can be robust and less sensitive to outliers. The validity and performance of our approach are tested using both syn-thetic data with outliers, and with real experiments to calibrate two Hitachi CCD cameras with H10x11E Fujinon active lenses. 1

Camera systems with zoom lenses are inherently more useful than those with passive lenses due to their flexibility and controllability. However, calibration techniques for active-cameras, still, lag behind those developed for calibration of passive-lens cameras. In this paper, we present a neural framework for zoom-lens camera calibration based on our proposed neurocalibration approach, which maps the classical problem of geometric camera calibration into a learning problem of a multi-layered feedforward neural network (MLFN). After discussing the features and advantages of the neurocalibration network, we present how this neural framework can capture the complex variations in the camera model parameters, both intrinsic and extrinsic, while minimizing the calibration error over all the calibration data across continuous ranges in the lens control space. The framework consists of a number of MLFNs learning concurrently, independently and cooperatively, the perspective projection transformation of the camera over its optical setting ranges. The calibration results of this technique applied to Hitachi CCD cameras with H10x11E Fujinon active lenses are reported.

Camera calibration is a primary crucial step in many computer vision tasks. We present a new neural approach for camera calibration. Unlike some existing neural approaches, our calibrating network can tell the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. The neurocalibration technique is shown to solve four different types of calibration problems that are found in computer vision applications. Moreover, it can be extended to the more difficult problem of calibrating cameras with automated active lenses. The validity and performance of our technique are tested with both synthetic data under different noise conditions and with real images. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.

This paper presents the neurocalibration approach as a new neural-based solution for the problem of camera calibration. Unlike some existing neural approaches, our calibrating network can match the perspective-projection-transformation matrix between the world 3D points and the corresponding 2D image pixels. Starting from random initial weights, the net can specify the camera model parameters satisfying the orthogonality constraints on the rotational transformation. In order to improve the accuracy of calibration results, the paper demonstrates the application of the neurocalibration technique to multi-image camera calibration. In such a case, many images are taken by the same camera but from different (rotated and/or translated) positions. Experiments have shown the accuracy and the efficiency of our neurocalibration technique.

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.