Although various camera calibration methods have been proposed, most of these methods cannot deliver accurate determination of the intrinsic camera parameters, because of the coupling errors existing between intrinsic and extrinsic camera parameters and the use of explicit distortion models. Here, we propose a model-free method for accurate camera calibration, which utilizes phase-shifted fringe patterns shown on a liquid crystal display (LCD) screen for estimating the intrinsic parameters of a camera and correcting lens distortion. Horizontal and vertical fringe patterns are consecutively displayed on the LCD screen, which is placed at two positions parallel to the camera sensor plane. These fringe patterns are captured by the camera from the front viewpoint for calculating the absolute phase maps. Then, the images with lens distortion can be transformed to the distortion-free images using an inverse mapping operation. Subsequently, the principal point coordinates are calculated according to the geometric imaging relationship between the positions of the two LCD screens. Finally, the focal length can be estimated using the similarity of triangles formed by the obtained principal point coordinates and the obtained phase maps at the two positions. Both simulated and real experiments are performed to verify the validity of the proposed method. The results demonstrate that this method not only successfully eliminates coupling errors but also perfectly corrects lens distortion.
Fringe projection profilometry is commonly used for three-dimensional (3-D) shape measurement. The gamma effect caused by nonlinear intensity response of projector-camera system makes ideal sinusoidal waveforms nonsinusoidal, leading to phase and 3-D shape errors. The existing phase-error compensation methods or gamma correction methods are complicated and time-consuming. An exponential fringe projection method is proposed based on principal component analysis to alleviate phase error without requiring any complicated prior and post data. The experimental results demonstrate that the proposed method decreases the phase error effectively with high quality in the measured surfaces and needs low computational cost by comparing with the existing state-of-the-art methods.
Infrared absorption spectroscopy has been widely used in the field of quantitative analysis. The overlapped spectral lines of different components makes the component recognition and concentration calculation of the mixture difficult to realize. In order to solve this problem, in this paper the analytic technology of infrared absorption spectrum has been researched to establish an effective, accurate and stable component identification method. The derivative spectrum of direct absorption spectral line is calculated to eliminate the influence of background and noise. The wavelet analysis method with appropriate wavelet basis and scale resolution is used to make the time-frequency analysis of the derivative spectrum to obtain the two dimensional time-frequency characteristics matrix. The correlation analysis in both time and frequency dimensions to the time-frequency characteristics matrix of different components and mixtures is researched to realize the component identification in combination with the morphological characteristics of the derivative spectral line. Experimental results show that the proposed method can effectively identify the target component from the mixture spectral line. The research provides a method and technical route for simultaneous detection and spectral analysis of multi-component.
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