Abstract SIMULATION, MEASUREMENT AND IMAGE ANALYSIS OF CORROSION INITIATION AND GROWTH RATE OF ALUMIUM 2024 AND STEEL 304 By Yan Fang, MS A thesis submitted in partial fulfillment of the requirements for the degree of M.S. of Mechanical and Nuclear Engineering at Virginia Commonwealth University. Virginia Commonwealth University, 2011 Major Director: Dr. Brian Hinderliter, Ph. D Associate professor, Mechanical and Nuclear Engineering Time: 11 a.m. Place: Room E3210 Engineering East Hall Date: Friday August 12, 2011 Corrosion initiation and growth rate are important properties in maintaining structural integrity, especially for surface and pit corrosion of common infrastructure and transportation metals. In this study, the surface corrosion pit initiation and growth rate on Aluminum 2024(common aerospace alloy) and Steel 304(common alloy for infrastructure) in different pH solutions was measured and values were analyzed by image analysis over a scheduled time. A MATLAB algorithm was developed for detecting the initiation and growth rate of pits as a function of time. The developed algorithm was validated with simulated specimen as well as experiments conducted corrosion specimen. Based on the result of obtained, the MATLAB algorithm predicts the right trends and power law radial corrosion pit growth rates and should be useful for corrosion initiation and growth predictions in various metals.

Zebrafish swimming behavior is used in a new, automated drug assay system as a biomarker to measure drug efficiency to prevent or restore hearing loss. This system records video of zebrafish larvae under infrared lighting using Raspberry Pi cameras and measures fish swimming behavior. This automated system significantly reduces the operator time required to process experiments in parallel. Multiple tanks, each consisting of sixteen experiments are operated in parallel. Once a set of experiments starts, all data transfer and processing operations are automatic. A web interface allows the operator to configure, monitor and control the experiments and review reports. Ethernet connects the various hardware components, allowing loose coupling of the distributed software used to schedule and run the experiments. The operator can configure the data processing to be done on the local computer or offloaded to a high-performance computer cluster to achieve even higher throughput. Computationally efficient image processing algorithms provided automated zebrafish detection and motion analysis. Quantitative assessment of error in the position and orientation of the detected fish uses manual data analysis by human observers as the reference. The system error in orientation and position is comparable to human inter-operator error.

Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process, since under- or over- estimation of this number may lead to incorrect unmixing for unsupervised methods. In this thesis we introduce a new method for determining the intrinsic dimension, using recent advances in Random Matrix Theory (RMT). This method is not sensitive to non-i.i.d. and correlated noise, and it is entirely unsupervised and free from any user-determined parameters. The new RMT method is mathematically derived, and robustness tests are run on synthetic data to determine how the results are a ected by: image size; noise levels; noise variability; noise approximation; spectral characteristics of the endmembers, etc. Success rates are determined for many di erent synthetic images, and the method is compared to two principal state of the art methods, Noise Subspace Projection (NSP) and HySime. All three methods are then tested on twelve real hyperspectral images, including images acquired by satellite, airborne and land-based sensors. When images that were acquired by di erent sensors over the same spatial area are evaluated, RMT gives consistent results, showing the robustness of this method to sensor characterisics.

<p>This thesis presents and examines a method that can potentially be used for extracting parameters from a manifold in a space. In the thesis the method is presented, and a potential application is described. The application is determining FAP-values. FAP-values</p><p>are used for parameterizing faces, which can e.g. be used to compress data when sending video sequences over limited bandwidth.</p>

<p>The cylinder liners surface is of major importance in an engine since it interacts with the piston rings and creates a tribologic system. This tribologic system has to be qualified and controlled in order to understand and control wear, oil consumption and shelf life. In this purpose, a program has been created in order to analyze SEM pictures and from them qualify the surface.</p><p>The aim of the project presented here has been to improve the preliminary steps leading to the image computation by standardizing the picture acquisition and improve the image filtering.</p>

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single is employed, although the algorithms are normally iterated over multiple scales, whereas phase-congruency methods utilize the phase from multiple scales simultaneously. This paper presents an extension to phase-difference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95% of the 120 tested cases.

The cylinder liners surface is of major importance in an engine since it interacts with the piston rings and creates a tribologic system. This tribologic system has to be qualified and controlled in order to understand and control wear, oil consumption and shelf life. In this purpose, a program has been created in order to analyze SEM pictures and from them qualify the surface. The aim of the project presented here has been to improve the preliminary steps leading to the image computation by standardizing the picture acquisition and improve the image filtering.

Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification.

This thesis addresses two important topics in developing a systematic space-time geometric approach to real-time, low-level motion vision. The first one concerns measuring of image flow, while the second one focuses on how to find low level features. We argue for studying motion vision in terms of space-time geometry rather than in terms of two (or a few) consecutive image frames. The use of Galilean Geometry and Galilean similarity geometry for this  purpose is motivated and relevant geometrical background is reviewed. In order to measure the visual signal in a way that respects the geometry of the situation and the causal nature of time, we argue that a time causal Galilean spatio-temporal scale-space is needed. The scale-space axioms are chosen so that they generalize popular axiomatizations of spatial scale-space to spatio-temporal  geometries. To be able to derive the scale-space, an infinitesimal framework for scale-spaces that respects a more general class of Lie groups (compared to previous theory) is developed and applied. Perhaps surprisingly, we find that with the chosen axiomatization, a time causal Galilean scale-space is not possible as an evolution process on space and time. However, it is possible on space and memory. We argue that this actually is a more accurate and realistic model of motion vision. While the derivation of the time causal Galilean spatio-temporal scale-spaces requires some exotic mathematics, the end result is as simple as one possibly could hope for and a natural extension of  spatial scale-spaces. The unique infinitesimally generated scale-space is an ordinary diffusion equation with drift on memory and a diffusion equation on space. The drift is used for velocity  adaption, the "velocity adaption" part of Galilean geometry (the Galilean boost) and the temporal scale-space acts as memory. Lifting the restriction of infinitesimally generated scale spaces, we arrive at a new family of scale-spaces. These are generated by a family of fractional differential evolution equations that generalize the ordinary diffusion equation. The same type of evolution equations have recently become popular in research in e.g. financial and physical modeling. The second major topic in this thesis is extraction of features from an image flow. A set of low-level features can be derived by classifying basic Galilean differential invariants. We proceed to derive invariants for two main cases: when the spatio-temporal  gradient cuts the image plane and when it is tangent to the image plane. The former case corresponds to isophote curve motion and the later to creation and disappearance of image structure, a case that is not well captured by the theory of optical flow. The Galilean differential invariants that are derived are equivalent with curl, divergence, deformation and acceleration. These  invariants are normally calculated in terms of optical flow, but here they are instead calculated directly from the the  spatio-temporal image. / QC 20110518