Moumen T. Ahmed

Neurocalibration: a neural network that can tell camera calibration parameters

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.

Novel surface registration using the grid closest point (GCP) transform

A novel approach has been developed for fast registration of two sets of 3-D curves or surfaces. The technique is an extension of Besl and Mackays (1992) iterative closest point (ICP) algorithm. This technique solves the computational complexity associated with the ICP algorithm by applying a novel grid closest point (GCP) transform and a genetic algorithm to minimize the cost function. A detailed description of the algorithm is presented along with a comparison of its performance versus several registration techniques. Two applications are presented in this paper. In the first, the algorithm is used to register 2-D head contours extracted from CT/MRI data to correct for possible mis-alignment caused by motion artifacts during scanning. In the second, the algorithm is used to register 3-D segments of the human jaw obtained using the shape from shading technique. Registration using the GCP/GA technique is found to be significantly faster and of comparable accuracy than two popular techniques in the computer vision and medical imaging literature.

3D reconstruction of the human jaw from a sequence of images

A novel approach is proposed to obtain a record of the patients 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.

A neural approach to zoom-lens camera calibration from data with outliers

Camera systems with zoom lenses are inherently more useful than those with passive lenses due to their flexibility 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 collecting calibration data, the calibration approach should be prepared to handle possible outliers in the data. We demonstrate how the calibration approach can be robust and less sensitive to outliers. The validity and performance of our approach are tested using both synthetic data with outliers, and with real experiments to calibrate Hitachi CCD cameras with H10 £ 11E Fujinon active lenses. q 2002 Published by Elsevier Science B.V.

Non-metric calibration of camera lens distortion

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.

CardEye: a 3D trinocular active vision system

CardEye is an experimental, trinocular, 3D active vision system. Our goal is to create a flexible, precise tool for active vision research. The system uses an agile trinocular vision head mounted on a robotic arm. It has five degrees of freedom: pan, tilt, roll, vergence and variable baseline in addition to the automated zoom and focus of the lenses. It utilizes an active lighting device to assist in the surface reconstruction process. In this paper we describe the architecture of the system together with its functionality.

Differential methods for nonmetric calibration of camera lens distortion

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.

A neural optimization framework for zoom lens camera calibration

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.

A neural approach for single- and multi-image camera calibration

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.

Comparative study of parallel vs. distributed genetic algorithm implementation for ATM networking environment

The objectives of this effort are: (1) to provide a faster (parallel) implementation to a previously implemented ATM network design simulator, and (2) to compare two different parallel and distributed computing (PDC) architectures in order to select one to be the architecture for this new parallel version. To improve the performance of the previously implemented GA, two parallel versions are developed using two different approaches. The first uses message passing interface (MPI) function calls embedded in C++ programs on a network of workstations. The second version is developed for a multiprocessor system using a C/C++ parallel compiler. This paper describes the approaches used to develop the parallel versions, the design of parallel applications and the results of all the experiments that were done to study the effect of all the design parameters on system performance.