Outlier removal method for the refinement of optical measured displacement field based on critical factor least squares and subdomain division

As a representative type of outlier, the abnormal data in displacement measurement often inevitably occurred in full-field optical metrology and significantly affected the further evaluation, especially when calculating the strain field by differencing the displacement. In this study, an outlier removal method is proposed which can recognize and remove the abnormal data in optically measured displacement field. A iterative critical factor least squares algorithm (CFLS) is developed which distinguishes the distance between the data points and the least square plane to identify the outliers. A successive boundary point algorithm is proposed to divide the measurement domain to improve the applicability and effectiveness of the CFLS algorithm. The feasibility and precision of the proposed method are discussed in detail through simulations and experiments. Results show that the outliers are reliably recognized and the precision of the strain estimation is highly improved by using these methods.

No-Reference Image Quality Assessment Of Large FLAIR MRI Datasets

<div>The study of neurodegenerative diseases have found promise through white matter lesions best visualized in FLAIR MRI; however, algorithms experience difficulty in generalizing to large multicenter datasets due to the variance of image quality and characteristics. This thesis presents a quality control tool that combines image quality assessment with outlier rejection algorithms; this tool is unique as it is specifically designed for large multicenter FLAIR MRI datasets. An image processing approach evaluates each volume by: intensity-based features, sharpness/blur-based features, signal- and contrast-to-noise ratios, noise field characteristics, motion artifact prevalence</div><div>and a total IQ score. The performance of this tool was evaluated on labelled ADNI and CCNA data reporting F1 scores of 0.82, and 0.85, respectively. Applications for this tool include potential rescan or longitudinal scanner study alongside the immediate application of outlier removal for</div><div>large FLAIR datasets.</div>

No-Reference Image Quality Assessment Of Large FLAIR MRI Datasets

<div>The study of neurodegenerative diseases have found promise through white matter lesions best visualized in FLAIR MRI; however, algorithms experience difficulty in generalizing to large multicenter datasets due to the variance of image quality and characteristics. This thesis presents a quality control tool that combines image quality assessment with outlier rejection algorithms; this tool is unique as it is specifically designed for large multicenter FLAIR MRI datasets. An image processing approach evaluates each volume by: intensity-based features, sharpness/blur-based features, signal- and contrast-to-noise ratios, noise field characteristics, motion artifact prevalence</div><div>and a total IQ score. The performance of this tool was evaluated on labelled ADNI and CCNA data reporting F1 scores of 0.82, and 0.85, respectively. Applications for this tool include potential rescan or longitudinal scanner study alongside the immediate application of outlier removal for</div><div>large FLAIR datasets.</div>

Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

A missing value is one of the factors that often cause incomplete data in almost all studies, even those that are well-designed and controlled. It can also decrease a study’s statistical power or result in inaccurate estimations and conclusions. Hence, data normalization and missing value handling are considered the major problems in the data pre-processing stage, while classification algorithms are adopted to handle numerical features. In cases where the observed data contained outliers, the missing value estimated results are sometimes unreliable or even differ greatly from the true values. Therefore, this study aims to propose the combination of normalization and outlier removals before imputing missing values on the class center-based firefly algorithm method (ON + C3FA). Moreover, some standard imputation techniques like mean, a random value, regression, as well as multiple imputation, KNN imputation, and decision tree (DT)-based missing value imputation were utilized as a comparison of the proposed method. Experimental results on the sonar dataset showed normalization and outlier removals effect in the methods. According to the proposed method (ON + C3FA), AUC, accuracy, F1-Score, Precision, Recall, and AUC-PR had 0.972, 0.906, 0.906, 0.908, 0.906, 0.61 respectively. The result showed combining normalization and outlier removals in C3-FA (ON + C3FA) was an efficient technique for obtaining actual data in handling missing values, and it also outperformed the previous studies methods with r and RMSE values of 0.935 and 0.02. Meanwhile, the Dks value obtained from this technique was 0.04, which indicated that it could maintain the values or distribution accuracy.

Geometric Modeling of Rosa roxburghii Fruit Based on Three-Dimensional Point Cloud Reconstruction

Fruit three-dimensional (3D) model is crucial to estimating its geometrical and mechanical properties and improving the level of fruit mechanical processing. Considering the complex geometrical features and the required model accuracy, this paper proposed a 3D point cloud reconstruction method for the Rosa roxburghii fruit based on a three-dimensional laser scanner, including 3D point cloud generation, point cloud registration, fruit thorns segmentation, and 3D reconstruction. The 3D laser scanner was used to obtain the original 3D point cloud data of the Rosa roxburghii fruit, and then the fruit thorns data were removed by the segmentation algorithm combining the statistical outlier removal and radius outlier removal. By analyzing the effects of five-point cloud simplification methods, the optimal simplification method was determined. The Poisson reconstruction algorithm, the screened Poisson reconstruction algorithm, the greedy projection triangulation algorithm, and the Delaunay triangulation algorithm were utilized to reconstruct the fruit model. The number of model vertices, the number of facets, and the relative volume error were used to determine the best reconstruction algorithm. The results indicated that this model can better reconstruct the actual surface of Rosa roxburghii fruit. The method provides a reference for the related application.

The use of B-splines to represent the topography of river networks

This work presents a new extension to B-Splines that enables them to model functions on directed tree graphs such as non-braided river networks. The main challenge of the application of B-splines to graphs is their definition in the neighbourhood of nodes with more than two incident edges. Achieving that the B-splines are continuous at these points is non-trivial. For both, simplification reasons and in view of our application, we limit the graphs to directed tree graphs. To fulfil the requirement of continuity, the knots defining the B-Splines need to be located symmetrically along the edges with the same direction. With such defined B-Splines, we approximate the topography of the Mekong River system from scattered height data along the river. To this end, we first test and validate successfully the method with synthetic water level data, with and without added annual signal. The quality of the resulting heights is assessed besides others by means of root mean square errors (RMSE) and mean absolute differences (MAD). The RMSE values are 0.26 m and 1.05 m without and with added annual variation respectively and the MAD values are even lower with 0.11 m and 0.60 m. For the second test, we use real water level observations measured by satellite altimetry. Again, we successfully estimate the river topography, but also discuss the short comings and problems with unevenly distributed data. The unevenly distributed data leads to some very large outliers close to the upstream ends of the rivers tributaries and in regions with rapidly changing topography such as the Mekong Falls. Without the outlier removal the standard deviation of the resulting heights can be as large as 50 m with a mean value of 15.73 m. After the outlier removal the mean standard deviation drops to 8.34 m.