Dili Zhang

Error analysis and optimization of camera calibration

The relation between the accuracy of calibrated TV camera parameters and the calibration condition is examined by applying a law of propagation, and the optimal calibration condition is proposed where an iterative method is applied to calibrate the parameter values. Furthermore, the variance of the estimated 3D information is determined quantitatively in the case of optimal calibration condition.<<ETX>>

3-D object pose estimation based on iterative image matching: Shading and edge data fusion

Based on model-driven image matching, the 3-D object pose is iteratively estimated. Shading images and edge images are synthesized from an abject model, and are matched, individually, with the input images by using a nonlinear least-squares method. The fusion of the shading and the edge information is achieved choosing the better of the two pieces of image matching.

3-D object pose estimation by shading and edge data fusion-simulating virtual manipulation on mental images

Human beings seem to recognize objects based on a kind of model-matching, i.e., a virtual manipulation on mental images. This paper presents a 3-D object pose estimation method simulating the human recognition scheme. Computer synthesizes not only an edge image but also a shading image from an object model. Then, it matches the two kinds of synthesized images with the inputted images individually by using a non-linear least-squares method, and estimates the pose parameter values. Finally, it chooses the better of the individually estimated poses. Thus, the fusion of the shading and the edge information is achieved. Since the two pieces of information complement each other, this method has the advantage of much higher robustness and accuracy of pose estimation than ordinary model-matching techniques which rely only on geometrical features such as vertices or edges.

Relationships between the Camera Calibration Accuracy and the Imaging Condition

For the 3 D measurements using the images taken with cameras, it is necessary to know the values of the cameras intrinsic parameters, such as the image coordinates of principal point, i.e., the projection of the projection center to the image plane, the principal distance between the principal point and the projection center, and the image distortion coefficients. In the 3D measurements, we need to estimate the calibration accuracy of the cameras intrinsic parameters. Therefore, the authors examined a two-plane calibration method with respect to the calibration accuracy. Paying attention of the fact that the image coordinate measurement errors of the fiducial points propagate to the calibrated intrinsic parameter values, and the magnitude of the error could be formulized according to the setup of the fiducial points, the authors presented many useful relationships between the calibration accuracies and the fiducial point setups.

Optimization of camera parameter calibration based on a new criterion with effective visual angle

The 3D information obtained from camera, actually 2D image, is given as visual angles, and, so, we should evaluate the accuracy of calibration based on the visual angles. When we evaluate the accuracy, the visual angle, which direction should we take as the reference? True values of the coordinates of the principal points have been ordinarily used as the reference of the visual angle. But, the kind of evaluation criterion makes us overestimate visual angle errors although it is simple, and conforms to a fail-safe principle. And, it also causes us an ill effect that we should change fiducial-chart setup to suit an optimal condition that is given to each camera according to principal distances. We propose a novel criterion for the visual angle evaluation: the calibrated coordinates of principal points are taken as the references of the visual angle. It yields useful results: it markedly decreases visual angle estimation errors, and it also makes the optimal setup condition not vary with the principal distance. Further more, we present a useful formula enabling us to estimate calibration error for every camera in advance.

Visual target-object search using Gabor transform

This paper presents an efficient method to search a target- object image appearing on an unknown scene image. The target-object is affine-transformed from the original model image, that is, translated, rotated and scaled. The Gabor transform is used to obtain the spectrum information from both the model and the scene images. The spectrum information of the model has the characteristic that the spectrum plane response regularly corresponding to the rotation and scale-change in real space. Using the characteristic, target-object can be correctly detected and the pose is also calculated by spectrum matching. Also the few Gabor functions can cover the whole frequency spectrum of the original image with less interference, very few numbers of features, that is, the Gabor-expansion coefficients, can represent the target-object. It result in the highly efficient calculation.

Pose-estimation error analysis of the L-shaped line segments for object recognition

This paper present a method to reduce the calculation costs of object pose measurements. They are estimated by matching L-shaped line-segments in 3D object models with those in 2D object images using a P3P solution. The L segments have the minimum pieces of information for the estimation, and, therefore, it produces a large amount of L segments both in the 3D object models and in the 2D object images, resulting in enormous correspondences, and P3P calculations. To solve the problem, we propose a strategy enabling to select fewer L segments: as an evaluation function, we utilizes an average of the estimation errors when observing the L segments from representative points on a geodesic dome. Furthermore, we deduce a useful approximate formula for L segments having various shapes.

Simple and accurate camera calibration method

A simple and accurate camera calibration method is presented in this paper, and the relation between accuracy of calibrated TV camera parameters and calibration condition is examined by applying a law of error propagation. The optimal calibration condition is proposed where an iterative method is applied to calibrate the parameter values. Furthermore, the variance of the estimated 3-D information is determined quantitatively in the case of the optimal calibration condition. These results are confirmed through experiments.