2D Bayesian automated tilted-ring fitting of disk galaxies in large HI galaxy surveys: 2DBAT

We present a novel algorithm based on a Bayesian method for 2D tilted-ring analysis of disk galaxy velocity fields. Compared to the conventional algorithms based on a chi-squared minimisation procedure, this new Bayesian-based algorithm suffers less from local minima of the model parameters even with highly multi-modal posterior distributions. Moreover, the Bayesian analysis, implemented via Markov Chain Monte Carlo (MCMC) sampling, only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature will be essential when performing kinematic analysis on the large number of resolved galaxies expected to be detected in neutral hydrogen (HI) surveys with the Square Kilometre Array (SKA) and its pathfinders. The so-called '2D Bayesian Automated Tilted-ring fitter' (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to derive rotation curves of galaxies. We explore 2DBAT performance on (a) artificial HI data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies, and (b) Australia Telescope Compact Array (ATCA) HI data from the Local Volume HI Survey (LVHIS). We find that 2DBAT works best for well-resolved galaxies with intermediate inclinations (20 deg < i < 70 deg), complementing three-dimensional techniques better suited to modelling inclined galaxies.Comment: Accepted for publication in MNRAS; 46 pages, 33 figure

Robust fitting of multiple curves

Ph.DDOCTOR OF PHILOSOPH

BENDING THE DOMING EFFECT IN STRUCTURE FROM MOTION RECONSTRUCTIONS THROUGH BUNDLE ADJUSTMENT

Structure from Motion techniques provides low-cost and flexible methods that can be adopted in arial surveying to collect topographic data with accurate results. Nevertheless, the so-called "doming effect", due to unfortunate acquisition conditions or unreliable modeling of radial distortion, has been recognized as a critical issue that disrupts the quality of the attained 3D reconstruction. In this paper we propose a novel method, that works effectively in the presence of a nearly flat soil, to tackle a posteriori the doming effect: an automatic ground detection method is used to capture the doming deformation flawing the reconstruction, which in turn is wrapped to the correct geometry by iteratively enforcing a planarity constraint through a Bundle Adjustment framework. Experiments on real word datasets demonstrate promising results

Robust statistical approaches for feature extraction in laser scanning 3D point cloud data

Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

An automated approach for extracting forest inventory data from individual trees using a handheld mobile laser scanner

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near )natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83-0.99, p> hard plotso << located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in "easy plots". By improving the algorithmic performances, the accuracy levels can be further increased by future research

Reconstruction of industrial piping installations from laser point clouds using profiling techniques

Includes abstract.Includes bibliographical references (leaves 143-152).As-built models of industrial piping installations are essential for planning applications in industry. Laser scanning has emerged as the preferred data acquisition method of as built information for creating these three dimensional (3D) models. The product of the scanning process is a cloud of points representing scanned surfaces. From this point cloud, 3D models of the surfaces are reconstructed. Most surfaces are of piping elements e.g. straight pipes, t-junctions, elbows, spheres. The automatic detection of these piping elements in point clouds has the greatest impact on the reconstructed model. Various algorithms have been proposed for detecting piping elements in point clouds. However, most algorithms detect cylinders (straight pipes) and planes which make up a small percentage of piping elements found in industrial installations. In addition, these algorithms do not allow for deformation detection in pipes. Therefore, the work in this research is aimed at the detection of piping elements (straight pipes, elbows, t-junctions and flange) in point clouds including deformation detection

Robust Vision-based Thermal Control Systems with Industrial Applications

As multimodal camera networks have been deployed in various environment, image fusion is playing a critical role for better visual perception and process parameter measurement. The objective of the dissertation is to design robust vision-based thermal control systems to tolerate uncertainties for industrial automaton. To be specific, two new methods have been developed, one for robust shape fitting in visual images and another for packet loss recovery in thermal images. Firstly, an adaptive curve fitting technique is proposed based on prediction error sum of squares for the sampled data set containing outliers. The method converges very fast and superaccuracy can be obtained under certain conditions when compared with other methods. The method is applied to find an optimal curve of casting dies in the visual images. Secondly, the thermal image loss generated by network traffic from camera nodes to fusion center is modeled as a Markov chain. A graph cuts method is proposed to recover the loss based on thermal pattern classification. Simulation results show that thermal information can be partially retrieved, which may greatly increase the robustness of a thermal management system. The proposed methods are tested with a laboratory die casting process simulator with two visual cameras and one thermal camera. A simple fuzzy PID controller is designed to integrate the visual sensors into a control loop. The experimental results show that the homogeneity of the temperature distribution in the die may become achievable through the vision based thermal control system

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications