Papers by Keyword: Least Square Fitting

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Authors: G.X. Jia, Xing Hua Qu, H. Gong, S.H. Ye
Abstract: This paper describes a method to characterizing the digital camera. The nonlinear relationship between the RGB signals generated by a digital camera and original image CIEXYZ values was obtained using the polynomial regression procedures. The reasonable structures of the polynomial were found for two digital cameras. The better number of polynomial terms was 19, yielding a modeling accuracy typically averaging 2.1~2.2 E ∆ units and maximally 9.5~10.9 E ∆ units. The experiments results showed that the polynomial regression could be used to characterize commonly digital camera.
Authors: Zheng Zhong Shi, Yi Jian Huang
Abstract: Aiming at drawbacks of current methods for predicting the screening efficiency of probability sieve, this paper proposed a method of predict and study the screening efficiency of probability sieve based on higher-order spectrum(HOS) analysis and support vector machines(SVMs). First setting up trispectrum model with the vibration signals, then fitting out polynomial with least square method using the data which get out by the reconstruct power spectrum. Finaly, using support vector machines to predicting the screening efficiency with the coefficient of the polynomial as the sample input. The results show that the relative errors are all less than 2.4% and the absolute errors are all less than 0.021, which is ideal for efficiency forecast.
Authors: Yong Liu, Ding Fa Huang, Yong Jiang
Abstract: Phase-shifting interferometry on structured light projection is widely used in 3-D surface measurement. An investigation shows that least-squares fitting can significantly decrease random error by incorporating data from the intermediate phase values, but it cannot completely eliminate nonlinear error. This paper proposes an error-reduction method based on double three-step phase-shifting algorithm and least-squares fitting, and applies it on the temporal phase unwrapping algorithm using three-frequency heterodyne principle. Theoretical analyses and experiment results show that this method can greatly save data acquisition time and improve the precision.
Authors: Ting Ting Zhang, Xin Wang, Yu Bin Wei, Chang Wang, Tong Yu Liu
Abstract: Carbon dioxide is one of the important signature gases in spontaneous combustion forecasting of coal goaf area. In the mine limited ventilation environment, concentration of CO2 directly affects the health of coal mines. So a new application of the least squares fitting method in Coal Mine gas monitoring was introduced. The optical fiber CO2 monitoring system which based on this method and tunable diode laser absorption spectroscopy (TDLAS) technology was used in Coal Mine beam tube system, and selected a near-infrared wavelength 1609.117nm fiber-coupled distributed feedback laser (DFB) as light source. Goaf area gas concentrations were directly calculated by the least squares fitting method which also simplified the calibration operation that used only one point to calibrate. Compared to electronic CO2 detector, this improved system has very high detection accuracy and stability. In the condition of 1.4m gas cell, the minimum detectable concentration is 0.05%, and the minimum detectable spectral absorption rate can up to , response time≤60s.
Authors: Yue Hong Sun, Zhao Ling Tao, Jian Xiang Wei, De Shen Xia
Abstract: For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.
Authors: Zi Zhi Lin, Si Hui Shu
Abstract: In this paper, we present an algorithm for surface approximation to the measured cloudy data with four boundaries. Several key techniques about this algorithm are also described, such as base surface construction, projection and weighted least square approximation. We use weighted least square to reduce the times of iterations, because the iteration is a very expensive and error prone process. We add a positive weight to each point, and the weight-adding algorithm is introduced as well. Increasing the weight onto this point will decrease the approximation error of the point. Finally, some examples of this algorithm demonstrate its effectiveness and validity.
Authors: Wen Guo Li, Shao Jun Duan
Abstract: We present a camera calibration method based on circle plane board. The centres of circles on plane are regarded as the characteristic points, which are used to implement camera calibration. The proposed calibration is more accurate than many previous calibration algorithm because of the merit of the coordinate of circle centre being obtained from thousand of of edge pionts of ellipse, which is very reliable to image noise caused by edge extraction algorithm. Experiments shows the proposed algorithm can obtain high precise inner parameters, and lens distortion parameters.
Authors: Zhi Wei Yu, Sheng Guo Cheng
Abstract: Abstract. The data of compaction test is processed by use of numerical method and least-squares fitting method respectively through MATLAB software. After a simple comparative analysis of the two results, this paper aims to reach the conclusion that when the distribution of test data points consistent with the characteristics of soil compaction, it is better and more accurate to use numerical method.
Authors: Guang Hua Chen, Wen Zhou, Feng Jiao Wang, Bin Jie Xiao, Sun Fang Dai
Abstract: The video images of road monitoring system contain noise, which blurs the difference between the lane and the background. The lane detection algorithm based on traditional Canny edge detector hardly detects the single-pixel lane accurately and it produces pseudo lane. The paper proposes an effective lane detection method based on improved Canny edge detector and least square fitting. The proposed method improves the dual-threshold selection of traditional Canny detector by using the histogram concavity analysis, which sets the optimal threshold automatically. The least square method is used to fit the feature points of detected edges to accurate and single-pixel wide lane. Experimental results show that the proposed method detects the lane of video images accurately in the noise environment.
Authors: Jie Hou, Zhi Tao Xiao, Fang Zhang
Abstract: Lane detection is a key technique for intelligent vehicle driving. Aiming at the detection performance of existing lane detection algorithms, based on least square fitting, we propose a lane detection algorithm for structural road. The lane videos are gotten by the monocular camera installed in the car. Image preprocessing is applied to improve image contrast and then the image is segmented by improved Otsu. At last, the current lanes are extracted and equations are rebuilt by the least square fitting. The experiment results show that the proposed method has better accuracy and robustness compared to existing lane detection algorithms.
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