新型分子高光谱成像系统性能分析及数据预处理

将分子成像技术和高光谱技术相结合,研制了基于AOTF(Acousto-optic Tunable Filters)的分子高光谱成像系统。系统由显微镜、分光仪、CCD镜头、图像数据采集卡和计算机等几部分组成。在综合考虑各功能部件的性能及相互的制约关系的基础上,分析了系统的性能指标,系统的光谱范围从550～1 000 nm,可采集200个波段,空间分辨率可达0.061 5 μm,光谱分辨率可达2 nm,当CCD工作在积分模式下采集速度可达到2.612 5 s·B-1,当CCD工作在非积分模式下可达到约0.11 μs·B-1。由于受系统光源和光路中透镜及传感器性能的影响,采集到的图像数据需要进行预处理,文中提出一种空间维和光谱维联合校正的灰度校正系数算法,并给出算法的具体实现。以白血病的血液作样本,通过对比校正前后的单波段图像、伪彩色图像和光谱曲线,说明校正算法的有效性,为后续的光谱图像数据分析提供了有效的数据。     

EQUINOX55型遥感傅里叶变换红外光谱系统的校正

利用遥感傅 里叶变换红外发射光谱，对红外辐射源的绝对光谱能量分布和热气体浓度进行测量时，首先必须对测得的红外辐射源的发射光谱强度进行校正，为此，需用已知温度的标准绝对黑体对系统进行校正，本文考察了黑体温度对EQUINOX55型遥感傅里叶变换红外光谱的响应函数的影响。结果表明，仪器响应函数随着温度的升高而增加，但当温度比较高时，仪器响应函数曲线变化趋小。     

Linearity of calibration curves: use and misuse of the correlation coefficient

The correlation coefficient is commonly used to evaluate the degree of linear association between two variables. However, it can be shown that a correlation coefficient very close to one might also be obtained for a clear curved relationship. Other statistical tests, like the Lack-of-fit and Mandel’s fitting test thus appear more suitable for the validation of the linear calibration model. A number of cadmium calibration curves from atomic absorption spectroscopy were assessed for their linearity. All the investigated calibration curves were characterized by a high correlation coefficient (r >0.997) and low quality coefficient (QC <5%), but the straight-line model was systematically rejected at the 95% confidence level on the basis of the Lack-of-fit and Mandel’s fitting test. Furthermore, significantly different results were achieved between a linear regression model (LRM) and a quadratic regression (QRM) model in forecasting values for mid-scale calibration standards. The results obtained with the QRM did not differ significantly from the theoretically expected value, while those obtained with the LRM were systematically biased. It was concluded that a straight-line model with a high correlation coefficient, but with a lack-of-fit, yields significantly less accurate results than its curvilinear alternative. Received: 15 January 2002 Accepted: 18 April 2002     

A novel planar calibration method using the iterative correction of control points information

In the camera calibration using translational planar object instead of 3D target, the skew affected by the imprecision in installation causes the object to deviate from the designated position, which produces errors to the world coordinates of control points and reduces the calibration accuracy. In this paper, a skew correction model is established to correct this skew. We note and prove that the skew bias of control point which is close to the fix point is tiny. According to the projection regulation, the pixel distance between two adjacent control points which is close to the fix point in skewed situation is a value extremely approximate to that in the ideal situation. Based on this property, we utilize the pixel distance to assign the image coordinates based on the fix point. The assigned values are then employed to estimate the skew factors, by which the initial camera parameters are optimized and lens distortion in the calibration images are corrected. This process is then repeated until convergence. Experiments based on real images prove that this method is more accurate than other methods without correcting skew. The parameters obtained by our method can be applied to the 3D reconstruction directly and effectively.     

A novel calibration approach to structured light 3D vision inspection

Structured light 3D vision inspection is a commonly used method for various 3D surface profiling techniques. In this paper, a novel approach is proposed to generate the sufficient calibration points with high accuracy for structured light 3D vision. This approach is based on a flexible calibration target, composed of a photo-electrical aiming device and a 3D translation platform. An improved algorithm of back propagation (BP) neural network is also presented, and is successfully applied to the calibration of structured light 3D vision inspection. Finally, using the calibration points and the improved algorithm of BP neural network, the best network structure is established. The training accuracy for the best BP network structure is 0.083 mm, and its testing accuracy is 0.128 mm.     

A vision measurement model of laser displacement sensor and its calibration method

Laser displacement sensors (LDSs) use a triangulation measurement model in general. However, the non-linearity of the triangulation measurement model influences the measurement accuracy of the LDS, and the geometric parameters calibration process of the components of the LDS is tedious. In this paper, we present a vision measurement model of the LDS based on the perspective projection principle. Furthermore, a corresponding calibration method is proposed. A planar target with featured lines is moved by a 2D moving platform to some preset known positions. At each position, the world coordinates of calibration points are obtained by the cross ratio invariance principle and the linear array camera of the LDS is used for collecting target images. The simulations verify the effectiveness of the proposed model and the feasibility of the calibration method. The experimental results indicate that the calibration method achieves a calibration accuracy of 0.026 mm. Compared with the traditional measurement model, the vision measurement model of the LDS is more comprehensive and avoids a linear approximation procedure, and the corresponding calibration method is easily complemented.     

基于SVM的混合气体分布模式红外光谱在线识别方法

针对混合气体组分浓度分析中海量训练样本的获取、分析精度及实时在线分析等问题,将支持向量机这一新的信息处理方法和红外光谱分析法结合,提出了混合气体分布模式的概念。在此基础上,采用先进行混合气体分布模式识别,然后再进行混合气体分析的思路,在大量调查的基础上,研究探索了实际应用中可能出现的混合气体分布模式,确定60种混合气体分布模式,共计6 000个混合气体红外光谱数据样本用于模型的训练与检验。采用SMO算法实现了减量和增量的在线学习,最终建立了基于SVM的混合气体分布模式红外光谱在线识别模型。模型由模式识别和结果输出2层组成,模式识别层完成混合气体模式分布模式识别任务;结果输出层由60个SVM校正模型组成,完成具体的浓度分析任务。实验结果表明,该方法对混合气体分布模式的正确识别率不低于98.8%,可在小样本条件下对混合气体的分布模式进行在线识别,可在线实时加入新的混合气体分布模式,具有实际应用价值。     

Near infrared spectroscopy of carbon dioxide I. OCO line positions

High-resolution near-infrared (4000-9000 cm⁻¹) spectra of carbon dioxide have been recorded using the McMath-Pierce Fourier transform spectrometer at the Kitt Peak National Solar Observatory. Some 2500 observed positions have been used to determine spectroscopic constants for 53 different vibrational states of the ¹⁶O¹²C¹⁶O isotopologue, including eight vibrational states for which laboratory spectra have not previously been reported. Calibration by simultaneous use of CO near 4200 cm⁻¹ and C₂H₂ near 6500 cm⁻¹ provides absolute line position accuracies of 6.0 × 10⁻⁵ cm⁻¹ (RMS) for strong, isolated transitions throughout the observed range. Fits with RMS errors <3.8 × 10⁻⁵ cm⁻¹ have been obtained for the 20013 ← 00001, 20012 ← 00001, and 20011 ← 00001 bands, RMS errors <6 × 10⁻⁵ cm⁻¹ have been obtained for the 30014 ← 00001, 30013 ← 00001, 30012 ← 00001, and 00031 ← 00001 bands, and RMS errors <5 × 10⁻⁴ cm⁻¹ for even the weakest fitted bands. This work reduces CO₂ near-infrared line position uncertainties by a factor of 10 or more compared to the 2000 HITRAN line list, which has not been modified since the comprehensive work of Rothman et al. [J. Quant. Spectrosc. Rad. Transfer 48 (1992) 537]. The new line list satisfies the line position accuracies required for the next generation of CO₂ remote sensing instruments, improves the capability of solar-viewing spectrometers to retrieve precise column CO₂ measurements, and provides a secondary frequency standard in the near-infrared.     

Univariate analytical calibration methods and procedures. A review

An original focus on univariate calibration as an experimental process of quantitative analysis is presented. A novel classification system is introduced against the background of the present situation concerning nomenclature of calibration methods. Namely, it has been revealed that four methods well-known in analytical chemistry: the conventional method, the internal standard method, the indirect method and the dilution method, can be split into those carried out in both the interpolative and the extrapolative mode. It is then shown that the basic procedures of all these methods can be modified including different approaches, such as matrix-matched technique, spiking the sample with a reactant, bracketing calibration, and others. For the first time (as compared to monographies dealing with univariate calibration) it is reviewed how the methods are mixed and integrated with one another thereby creating new calibration strategies of extended capabilities in terms of enhanced resistance to the interference and non-linear effects – as the main sources of systematic calibration errors. As additional novelty, rationally possible combinations of the calibration methods – not met hitherto in the literature – have been predicted. Finally, some general rules relating to calibration are formulated and the main calibration problems that still need to be solved are displayed.     

Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis

New multivariate calibration methods and other processes are being developed that require selection of multiple tuning parameter (penalty) values to form the final model. With one or more tuning parameters, using only one measure of model quality to select final tuning parameter values is not sufficient. Optimization of several model quality measures is challenging. Thus, three fusion ranking methods are investigated for simultaneous assessment of multiple measures of model quality for selecting tuning parameter values. One is a supervised learning fusion rule named sum of ranking differences (SRD). The other two are non-supervised learning processes based on the sum and median operations. The effect of the number of models evaluated on the three fusion rules are also evaluated using three procedures. One procedure uses all models from all possible combinations of the tuning parameters. To reduce the number of models evaluated, an iterative process (only applicable to SRD) is applied and thresholding a model quality measure before applying the fusion rules is also used. A near infrared pharmaceutical data set requiring model updating is used to evaluate the three fusion rules. In this case, calibration of the primary conditions is for the active pharmaceutical ingredient (API) of tablets produced in a laboratory. The secondary conditions for calibration updating is for tablets produced in the full batch setting. Two model updating processes requiring selection of two unique tuning parameter values are studied. One is based on Tikhonov regularization (TR) and the other is a variation of partial least squares (PLS). The three fusion methods are shown to provide equivalent and acceptable results allowing automatic selection of the tuning parameter values. Best tuning parameter values are selected when model quality measures used with the fusion rules are for the small secondary sample set used to form the updated models. In this model updating situation, evaluation of all possible models, thresholding, and iterative SRD performed equivalently for the three fusion rules with TR and PLS performed worse. While the application is model updating, the fusion processes are applicable to other situations requiring selection of multiple tuning parameter values.