Kaset Sirisantisamrid

A simple technique to determine calibration parameters for coplanar camera calibration

Coplanar camera calibration is the process of determining the extrinsic and intrinsic parameters of camera, where a set of image and two-dimensional (2D) control points are known. This paper presents a simple and efficient technique to compute the calibration parameters for coplanar calibration. Computing of the calibration parameters is directly performed on computer image array in the frame buffer. Thereby, the image center can simultaneously computed together with other parameters. However, the parameters can be solved using available three orthogonality constraints, where the scale factor is assumed as one and the lens distortion is not considered. The proposed algorithm is tested with noise-free synthetic image and synthetic image corrupted with known noise. The results show that the proposed technique is accurate and robust to noise.

An Algorithm for Coplanar Camera Calibration

This paper proposes an algorithm to determine the camera parameters for coplanar camera calibration. The proposed method requires only one image to compute the camera parameters. The intermediate parameters defined in terms of the camera parameters are determined by a linear method. Then, the final intermediate parameters and the lens distortion coefficients are computed iteratively using a nonlinear optimization method in which the parameters obtained from the linear method are used as initial parameters. Finally, the camera parameters are determined from the final intermediate parameters. The proposed method is tested and compared with the conventional method using the synthetic images. The experimental results show that the camera parameters determined by the proposed method give accurate and stable.

Determine Calibration Parameters with Satisfied Constraints for Coplanar Camera Calibration

Coplanar camera calibration is the process of determining a set of calibration parameters that describe the mapping between 2D control points and 2D image coordinates. However, the optimal solution for coplanar calibration can be obtained by solving a set of constrained optimization problem. This paper presents a technique to determine the calibration parameters for coplanar calibration, where a set of orthogonality constraints is fully satisfied. The solving of camera calibration consists of two steps. The first step is an estimating of calibration parameters using a closed form solution based on distortion free. In the second step, the parameters estimated in first step are iteratively improved by nonlinear optimization method, where the objective function is enforced to satisfy the constraints during optimization process. The computing of parameters is directly performed on computer image array by assuming that scale factor is equal to 1. Consequently, the image center can be simultaneously computed with the other parameters. The proposed technique is tested with a synthetic image that known distortion. The result shows that the proposed technique gives sufficient accuracy.

A simple technique for coplanar camera calibration

This paper proposes a simple technique for camera calibration under assumption that a calibration pattern is on world coordinate system. The proposed method consists of two steps. In the first step, the intermediate parameters, lens distortion coefficients, principal point and the distances between sensors are computed by an iterative linear method. Then, the camera parameters are determined from the obtained intermediate parameters. In the experimental results, the proposed method gives the accurate and stable camera parameters for change rotation angles when compared with the conventional method.

An influential principal point on camera parameters

This paper proposes a method of camera calibration and the influence of principal point on camera parameters. The camera parameters are represented in terms of a set of intermediate parameters. The intermediate parameters and lens distortion coefficients are iteratively computed. The image coordinates are rectified on each cycle of iteration. Once the iteration is terminated, the initial parameters are computed from the obtained intermediate parameters. Then, the camera parameters and distortion coefficients are iteratively computed from the obtained initial parameters and the image coordinates are rectified too. In the experimental results, the proposed method is tested by change principal point to observe the influential principal point on camera parameters and compared the results with the conventional method.

Determination of Initial Parameters with Noise Robustness for Coplanar Calibration

This paper discusses a method to determine initial parameters with noise robustness for coplanar calibration. Most conventional methods are assumed that the image center is known in advance. The determination of initial parameters of image corrupted with quantization noise becomes unstable under the above assumption. In this paper, a method to determine the stable initial parameters of coplanar calibration is proposed. In order to show the effectiveness of the proposed method, the method is tested on synthetic images corrupted with known noise. The experimental results show that the proposed method is robust to noise

A Determination Method for Initial Values of Coplanar Camera Calibration Parameters

This paper discusses a determination method for initial values of coplanar camera calibration parameters. In the conventional methods, it is assumed that the image center is ignored or known in advance. The determination of the initial calibration parameters of image corrupted with quantization noise and lens distortion becomes sometimes unstable under the above assumption. In this paper, a method to find the stable initial values of the calibration parameters is proposed under assumption on the aspect ratio of the image. In order to show the effectiveness of the proposed method, the comparison results with the conventional method are shown. It was found from the experimental results that the proposed method gives more accurate results than those of the conventional methods

A realization of FIR system characterizing outline of fingers and its application to person identification

This paper discusses on a realization of FIR system characterizing outline of fingers of a single hand and its application to person identification. First, a hand image is obtained from the image acquisition system. Secondly the normalized outline of the fingers with rotation invariance is extracted from the hand image using KL expansion. Thirdly the horizontal and vertical components of the outline of the fingers are expanded into the Fourier series, respectively. Furthermore FIR system is realized by considering the absolute values of the Fourier coefficients (AFC) as the input and output, respectively. Then a person can be identified by evaluating the difference between the AFC for horizontal components of the outline of the fingers for reference person and the output of the FIR system to the input of AFC for vertical components of the outline of the fingers for the person to be identified. Finally, person identification experiments were performed for 2180 hand images obtained from 109 persons. As the results FRR (false rejection) and FAR (false acceptance) error rates were 1.70% and 0.74%, respectively.

A personal identification method based on finger’s outline

We propose a personal identification method based on fingerpsilas outline. In this paper first, we extract the fingerpsilas outline from a hand image acquired by the image acquisition system. Secondly we normalize the fingerpsilas outline by expanding the horizontal and vertical components into the KL expansion. The resulting fingerpsilas outline has a rotation invariant feature. Thirdly we expand the horizontal and vertical components of the normalized fingerpsilas outline into the Fourier series, respectively. Furthermore we consider a FIR system having the absolute values of the Fourier coefficients (AFC) as the input and output, respectively. It is considered that the FIR system represents the fingerpsilas outline feature. Then we can identify a person by evaluating the difference between the impulse responses of the FIR systems corresponding to the reference and the person to be identified. Finally, personal identification experiments were performed for 2180 hand images obtained from 109 persons. As the results FRR (false rejection) and FAR (false acceptance) error rates were 1.05% and 0.09%, respectively.