Image Moment Method Research Articles

Application of Tchebichef image moment method for simultaneous quantitative analysis of multiple components in B-vitamins

Application of Tchebichef image moment method for simultaneous quantitative analysis of multiple components in B-vitamins

An exhaustive analysis of the use of image moments for second-order calibration. A comparison with multivariate curve resolution-alternating least-squares

Backgroundthe chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). Recently, an alternative procedure was described based on the estimation of image moments for each data matrix and subsequent application of multiple linear regression after suitable variable selection. ResultsThe analysis of both simulated and experimental data leads to the conclusion that the image moment method, although can cope with chromatographic lack of reproducibility across injections, it only performs well in the absence of uncalibrated interferents. MCR-ALS, on the other hand, provides good analytical results in all studied situations, whether the test samples contain uncalibrated interferents or not. SignificanceThe results are useful to assess the real usefulness of newly proposed methodologies for second-order calibration in the case of chromatographic-spectral data sets, especially when samples contain unexpected chemical constituents.

Eigenspectra of solar active region long-period oscillations

Context. We studied the low-frequency ≲0.5 h−1 (long-period ≳2 h) oscillations of active regions (ARs). The investigation is based on an analysis of a time series built from Solar Dynamics Observatory/Helioseismic and Magnetic Imager photospheric magnetograms and comprises case studies of several types of AR structures. Aims. The main goals are to investigate whether ARs can be engaged in long-period oscillations as unified oscillatory entities and, if so, to determine the spectral pattern of such oscillations. Methods. Time series of characteristic parameters of the ARs, such as, the total area, total unsigned radial magnetic flux, and tilt angle, were measured and recorded using the image moment method. The power spectra were built out of Gaussian-apodised and zero-padded datasets. Results. There are long-period oscillations ranging from 2 to 20 h, similarly to the characteristic lifetimes of super-granulation, determined from the datasets of the AR total area and radial magnetic flux, respectively. However, no periodicity in tilt angle data was found. Conclusions. Whatever nature these oscillations have, they must be energetically supported by convective motions beneath the solar surface. The possible interpretations can be related to different types of magnetohydrodynamic oscillations of the multi-scale structure of the AR magnetic field, which is probably linked with the characteristic turnover timescales of the super-granulation cells. The presence of oscillations in the radial magnetic flux data may be connected to periodic flux emergence or cancellation processes.

A convolutional neural network approach for reconstructing polarization information of photoelectric X-ray polarimeters

This paper presents a data processing algorithm with machine learning for polarization extraction and event selection applied to photoelectron track images taken with X-ray polarimeters. The method uses a convolutional neural network (CNN) classification to predict the azimuthal angle and 2-D position of the initial photoelectron emission from a 2-D track image projected along the X-ray incident direction. Two CNN models are demonstrated with data sets generated by a Monte Carlo simulation: one has a commonly used loss function calculated by the cross entropy and the other has an additional loss term to penalize nonuniformity for an unpolarized modulation curve based on the $H$-test, which is used for periodic signal search in X-ray/$\gamma$-ray astronomy. The modulation curve calculated by the former model with unpolarized data has several irregular features, which can be canceled out by unfolding the angular response or simulating the detector rotation. On the other hand, the latter model can predict a flat modulation curve with a residual systematic modulation down to $\lesssim1$%. Both models show almost the same modulation factors and position accuracy of less than 2 pixel (or 240 $\mu$m) for all four test energies of 2.7, 4.5, 6.4, and 8.0 keV. In addition, event selection is performed based on probabilities from the CNN to maximize the polarization sensitivity considering a trade-off between the modulation factor and signal acceptance. The developed method with machine learning improves the polarization sensitivity by 10-20%, compared to that determined with the image moment method developed previously.

The deficiencies in filtering of the information at early levels of perception determine cognitive impairments in schizophrenia

Calibration model transfer is a practical application. In this contribution, we proposed the common model instead of model transfers to avoid the complex corrections for the obtained calibration model. The important chemical features of target components are extracted by Tchebichef image moment method based on the near infrared (NIR) three-dimensional spectra constructed from the determination of different spectrometers. Then the common models are established with stepwise regression and used to the determination of single spectrometer. The proposed approach was applied to the quantitative analysis of target components in mixtures using two datasets including the pharmaceutical samples (measured on two NIR spectrometers with the same type) and corn samples (measured on three NIR spectrometers with the different types), and the satisfactory results were obtained. Furthermore, multi-way partial least squares method was carried out and compared with the proposed approach. This study indicates that our approach is effective, accurate and reliable, and the common quantitative models can reveal the chemical feature information of target components in samples measured on whether the same or different types spectrometers, which provides a convenience for the application of NIR spectroscopy.

The common quantitative model for the determination of multiple near infrared spectrometers

Calibration model transfer is a practical application. In this contribution, we proposed the common model instead of model transfers to avoid the complex corrections for the obtained calibration model. The important chemical features of target components are extracted by Tchebichef image moment method based on the near infrared (NIR) three-dimensional spectra constructed from the determination of different spectrometers. Then the common models are established with stepwise regression and used to the determination of single spectrometer. The proposed approach was applied to the quantitative analysis of target components in mixtures using two datasets including the pharmaceutical samples (measured on two NIR spectrometers with the same type) and corn samples (measured on three NIR spectrometers with the different types), and the satisfactory results were obtained. Furthermore, multi-way partial least squares method was carried out and compared with the proposed approach. This study indicates that our approach is effective, accurate and reliable, and the common quantitative models can reveal the chemical feature information of target components in samples measured on whether the same or different types spectrometers, which provides a convenience for the application of NIR spectroscopy.

THMGUI: A user-friendly Matlab graphical user interface of Tchebichef-Hermite moment method for handing three-dimensional landscapes

This paper describes the development and implementation of THMGUI, a graphical user interface (GUI) for implementing the Tchebichef-Hermite moment (THM) method for quantitative analysis purpose on the basis of three-dimensional (3D) landscapes. It consists of four modules, namely “Import data”, “Image moment method”, “Splitting” and “Regression”. The THMGUI allows a user to apply the implemented methods in an easy way, in which the characteristic features acquisition, model establishment and its subsequent application in the analysis of unknown samples are straightforward from the interface. Moreover, the established models that involve the statistical parameters, and prediction results are conveniently managed through THMGUI shells and can be presented intuitively. For illustrating, an example involving the quantitative determination of two components on the basis of LC-MS 3D landscapes is presented.

Behavioristic image-based pose control of mobile manipulators using an uncalibrated eye-in-hand vision system

In the execution of material handling, the mobile manipulator is controlled to reach a station by its mobile base. This study adopts an uncalibrated eye-in-hand vision system to provide visual information for the manipulator to pick up a workpiece on the station. A novel vision-guided control strategy with a behavior-based look-and-move structure is proposed. This strategy is based on six image features, predefined by image moment method. In the designed neural-fuzzy controllers with varying learning rate, each image feature error is taken to generate intuitively one DOF motion command relative to the camera coordinate frame using fuzzy rules, which define a particular visual behavior. These behaviors are then fused to produce a final command action to perform grasping tasks using the proposed behavior fusion scheme. Finally, the proposed control strategy is experimentally applied to control the end-effector to approach and grasp a workpiece in various locations on a station.

Tchebichef-Hermite image moment method: A novel tool for chemometric analysis of three-dimensional spectra

Image moment methods have been applied to the qualitative and quantitative analyses in analytical chemistry owing to their availability. As another attractive image moment method, Tchebichef-Hermite moment (THM) method was proposed for the first time in this work, which originated from the Tchebichef moment method and Hermite moment method. The performances of the THM method were tested with two instances for the quantitative analysis of multiple target compounds on the basis of HPLC-PAD and LC-MS three-dimensional (3D) spectra, respectively. Experimental results indicate that the THM method not only inherits the common advantages of these discrete orthogonal moments to deal with some fundamental challenges (such as partially overlapped signals, uncalibrated interferences, peak shifts and baseline drifts) during the analytical process of different kinds of 3D spectra, but also has its unique superiority in information extraction ability that simplify the determination of optimum maximal orders in moment methods. Compared with the previously used Tchebichef moment method, THM method is much more convenient and efficient.

Krawtchouk image moment method for the simultaneous determination of three drugs in human plasma based on fluorescence three-dimensional spectra

The interference signals and overlapped peaks are common phenomena in fluorescence determination, which seriously influence the accuracy and reliability of analytical results. In this paper, Krawtchouk image moment method was introduced to the analysis of fluorescence three-dimensional (3D) spectra and applied to the quantitative analysis of three drugs including nicotinic acid, metoprolol and amlodipine in human plasma. Without any pretreatment to the obtained spectra, Krawtchouk moments were directly calculated on the grayscale images of 3D spectra, and the quantitative linear models for the three drugs were established by stepwise regression, respectively. The determination coefficients (R) were more than 0.9906. The correlation coefficients of leave-one-out cross-validation (Rcv) were more than 0.9256. The precisions (RSD, %) of inter-day and intra-day variations were less than 8.4% and 3.1%, separately. The recovery was from 101.84% to 107.88%. The limit of quantification and the limit of detection were 0.12μgmL−1 and 0.43μgmL−1, respectively. All the statistical parameters supported that the obtained models performed well and the proposed method was accurate and reliable. Our study indicates that Krawtchouk image moments with the powerful abilities of multi-resolution and extracting local information can be applied to the simultaneous determination of multi-target compounds in plasma based on fluorescence 3D spectra.

The image moment method for the limited range CT image reconstruction and pattern recognition

Moment properties of the Radon transform have been discussed. A new concept of the image moment in the Radon transform has been introduced and described. A new approach to reconstruct the image from the projections within a limited range, called the image moment method, has been proposed. The new method has been validated through computer simulations.