Crack detection using enhanced thresholding on UAV based collected images

© 2018 Australasian Robotics and Automation Association. All rights reserved. This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings

A Study On The Synthesis And Properties Of Gold Nanoparticles And Their Applications To Surface Enhanced Raman Spectroscopy

Surface enhanced Raman substrates represent a critical step in the sensitivity augmentation of Raman spectroscopy, but due to the time and expertise currently required in the synthesis process, they have not been fully realized within an industrial setting. This thesis aims to investigate a new technique in surface enhanced Raman spectroscopy (SERS) substrate fabrication designed to decrease synthesis time and map the most effective growth parameters for a successful Raman intensity boost. Sputter physical vapor deposition was utilized to deposit a gold (Au) layer on an insulating substrate, and annealing treatments were applied to agglomerate the Au atoms into the sought after nanostructures conducive to the SERS effect. Substrates were characterized with a variety of techniques including scanning electron microscopy, Raman spectroscopy, and atomic force microscopy, as well as several others. Physical morphologies of the nano features are summarized as well as their impact upon spectroscopic behavior. Surface enhanced Raman intensity boost is also documented and linked back to growth parameters in order to determine effective deposition methods for a successful SERS substrat

Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Advanced Imaging Techniques for Point-Measurement Analysis of Pharmaceutical Materials

Drugs are an essential element protecting human lives from many diseases such as cancer, diabetes, and cardiovascular disorders. One of the highlights in drug development in recent years is the establishment of rational drug design: a collection of various multi-disciplinary approaches that at the core, focus on designing molecules with specific properties for identified targets and biomolecules with known functional roles and structural information. The candidate molecules will then go through a series of examinations to characterize their physiochemical properties, and an iterative process is used to improve the design of the drug to achieve desirable attributes. The time consuming and highly expensive nature of drug development constantly calls for new analytical techniques that have increasingly higher throughput, faster analysis speed, richer chemical and structural information, and lower risk and cost. Conventional analytical methods for pharmaceutical materials, such as X-ray diffraction analysis and Raman spectroscopy, often suffer from prolonged measurement time. In many cases, the identification of regions of interest within the sample is non-trivial in itself. Nonlinear optical imaging techniques, including second harmonic generation (SHG) microscopy and two-photon excited ultraviolet fluorescence (TPE-UVF) microscopy were developed as fast, real-time, and non-destructive methods for selective identification and characterization of crystalline materials present in pharmaceutical samples. These techniques were integrated with synchrotron X-ray diffraction analysis and Raman spectroscopy to significantly reduce the overall measurement time of these structure characterization techniques. In the meanwhile, with the now increased speed of measurement, the amount of experimental data acquired per unit time has also drastically increased. The rate at which data are analyzed, digested, and interpreted is becoming the bottleneck in data-driving decision-making. Novel electronics that only collect data at the most information-rich time points were employed to significantly increase the signal-to-noise ratio (SNR) during data acquisition, reducing the total amount of data needed for material characterization. Advanced sampling algorithms to reduce the total amount of measurements required for perfect data space reconstruction, automated programs for data acquisition and analysis, and efficient data analysis algorithms based on machine learning were developed for accelerated data processing for nonlinear optical imaging analysis, Raman spectra processing, and X-ray diffraction indexing