Three dimensional image processing and analysis, particularly, the imaging and reconstruction of an environment in three dimensions  has received significant attention, interest and concern  during the recent years. In the light of this background, this research study intends to provide an efficient way to reconstruct an irregular surfaced object, for example "the sole of a shoe", with good precision at a low cost using XBOX Kinect V2.0 sensor. Three dimensional reconstruction can be achieved either by using active or passive methods. Active methods make use of light source such as lasers or infra-red emitters for scanning a given environment and measuring the depth, to create a depth map. In contrast, in passive methods, colour images of the environment in different perspectives are used to create a three dimensional model of the environment. In this study, an active method using a set of depth maps of the object of interest is implemented, where the object of interest is represented by a sole of a shoe. Firstly, a set of depth maps of the object of interest are acquired in different perspectives. The acquired depth maps are first pre-processed for removing any outliers in the data acquired and are then enhanced. Enhanced depth maps are converted into 3D point clouds using the intrinsic parameters of the Kinect sensor. These obtained point clouds are subsequently registered into a single position using the Iterative Closest Point(ICP) algorithm. Aligned point clouds of the object of interest are then merged to form a single dense point cloud of the object of interest. Analysis of the generated single dense point cloud has shown that accurate 3D reconstruction of the real object of the interest has been achieved.

Hypertension is a major risk factor for coronary artery disease, stroke, and kidney disease. Many studies suggest that elevated intramural stresses caused by hypertension may stimulate inflammatory changes, but little has been done to ascertain whether inflammation and stress are spatially correlated. Such correlations are a first step in identifying the mechanisms that may relate intramural stress to disease so that more effective clinical treatments may be developed. Arterial branches exhibit local stress peaks and are focal points for the onset of disease. They are thus a logical place to examine whether high stresses spatially correlate with increased inflammation. This research seeks to 1) develop a histology-based method to reconstruct small arterial branches; 2) use finite element analysis to evaluate intramural stresses where experimental testing is of limited use; 3) quantify biological measures of inflammation; and 4) visually and statistically compare the distribution of stress with the distribution of inflammation. Hypertension was induced in Sprague-Dawley rats by implanting Angiotensin II pumps for 7 days or 21 days. Normotensive rats were used as controls. To preserve morphology the mesentery was pressure-fixed in situ, harvested, processed, and embedded in glycol methacrylate resin. Branch geometry was reconstituted from serial sections. This involved: correcting deformations caused by sectioning; aligning sections into an image stack; identifying vessel boundaries; creating a surface suitable for finite element analysis; reducing the branch geometry to a midplane surface; and using Ansys (Ansys, Inc.) to mesh the midplane surface with a variable-thickness shell element. The pattern of inflammation was characterized by measuring the local density of monocytes and macrophages. Cell density was expressed as a distribution on the branch surface, which simplified visualization and facilitated statistical comparisons of inflammation with stress. Both intramural stresses and inflammation were greater near branches during hypertension. In most cases, however, high stresses and high cell density were not spatially collocated. The onset of an adaptive response may reduce the strength of this correlation. Maximal wall tension, defined as the maximal midplane stress multiplied by the wall thickness, was elevated near branches and strongly correlated with cell density.

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Computational imaging di↵ers from traditional imaging system by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D- 3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color (a “rainbow”). For reconstruction, we derive an image formation model for recovering stationary 3D particle positions. 3D velocity estimation is achieved with a variant of 3D optical flow that accounts for both physical constraints as well as the rainbow image formation model. This velocity field can be used to refine the position estimate by adding physical priors that tie together all the time steps, forming a joint reconstruction scheme. In the second approach, we study the problem of reconstructing the 3D shape of underwater environments. The distortions from the moving water surface provide a changing parallax for each point on the underwater surface. We utilize this observation by jointly estimating both the underwater geometry and the dynamic shape of the water surface. To this end, we propose a novel di↵erentiable framework to tie together all parameters in an integrated image formation model. To our knowledge, this is the first solution that is capable to simultaneously retrieve the structure of dynamic water surfaces and static underwater scene geometry in the wild.

The art of reconstructing a real-world scene digitally has been on the mind of researchers for decades. Recently, it has attracted more and more attention from companies seeing a chance to bring this kind of technology to the market. Digital reconstruction of buildings in particular is a niche that has both potential and room for improvement. With this background, this thesis will present the design and evaluation of a pipeline made to find and correct approximately flat surfaces in architectural scenes. The scenes are 3D-reconstructed triangle meshes based on RGB images. The thesis will also comprise an evaluation of a few different components available for doing this, leading to a choice of best components. The goal is to improve the visual quality of the reconstruction. The final pipeline is designed with two blocks - one to detect initial plane seeds and one to refine the detected planes. The first block makes use of a multi-label energy formulation on the graph that describes the reconstructed surface. Penalties are assigned to each vertex and each edge of the graph based on the vertex labels, effectively describing a Markov Random Field. The energy is minimized with the help of the alpha-expansion algorithm. The second block uses heuristics for growing the detected plane seeds, merging similar planes together and extracting deviating details. Results on several scenes are presented, showing that the visual quality has been improved while maintaining accuracy compared with ground truth data. / Konsten att digitalt rekonstruera en verklig miljö har länge varit intressant för forskare. Nyligen har området även tilldragit sig mer och mer uppmärksamhet från företag som ser en möjlighet att föra den här typen av teknik till produkter på marknaden. I synnerhet är digital rekonstruktion av byggnader en nisch som har både stor potential och möjlighet till förbättring. Med denna bakgrund så presenterar detta examensarbete designen för och utvärderingen av en pipeline som skapats för att detektera och rätta till approximativt platta regioner i arkitektoniska miljöer. Miljöerna är 3D-rekonstruerade triangelmeshar skapade från RGB-bilder. Examensarbetet omfattar även utvärdering av olika komponenter för att uppnå detta, som avslutas med att de mest lämpliga komponenterna presenteras. Målet i korthet är att förbättra den visuella kvaliteten av en rekonstruerad modell. Den slutgiltiga pipelinen består av två övergripande block - ett för att detektera initiala plan och ett för att förbättra de funna planen. Det första blocket använder en multi-label energiformulering på grafen som beskriver den rekonstruerade ytan. Straffvärden tilldelas varje vertex och varje båge i grafen baserade på varje vertex label. På så sätt beskriver grafen ett Markov Random Field. Energin är sedan minimerad med alpha-expansion-algoritmen. Det andra blocket använder heuristiker för att låta planen växa, slå ihop närliggande plan och för att extrahera avvikande detaljer. Resultat på flera miljöer presenteras också för att påvisa att den visuella kvaliteten har förbättrats utan att rekonstruktionens noggrannhet har försämrats jämfört med ground truth-data.

Poster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona. Recipient of the 2016 Katheryne B. Willock Library Research Award. / The nervous system is composed of two types of cells: neurons and glia. In neuronal circuits, neurons communicate through synapses and glia play a crucial modulatory role. To modulate chemical reuptake, glia send processes close to synapses and many glia directly appose or ensheathe a synapse. This structural motif is one of the elements often included in describing a vertebrate tripartite synapse, which includes a bidirectional functional neuron-glia relationship. The exact nature of this neuron-glia communication is not well understood. In the invertebrate fruit fly, we have also found that particular neurons and glia also have a bidirectional functional relationship. This allows us to ask new questions about glial morphology. Throughout multiple images, I identified particular neuronal synapses and surrounding glia. After creating a 3D reconstruction, I measured the distance between a particular neuronal synapse and its closest glial process. Interestingly, the neuronal synapses were not directly apposed or ensheathed by glia, and the distance to the closest glial process varied one-hundred-fold. With variable distance, functional communication is consistently present. These findings provide important insight into invertebrate neuron-glia communication, and offer new avenues to investigate the structural neuron-glia relationships that are required for reciprocal signaling between the two cell classes.

Since most people now have a high-performing computing device with an attached camera in their pocket, in the form of a smartphone, robotics and computer vision researchers are thrilled about the possibility this creates. Such devices have previously been used in robotics to create 3D maps of environments and objects by feeding the camera data to a 3D reconstruction algorithm. The big downside with smartphones is that their cameras use a different sensor than what is usually used in robotics, namely a rolling shutter camera.These cameras are cheaper to produce but are not as well suited for general 3D reconstruction algorithms as the global shutter cameras typically used in robotics research. One recent, accurate and performance effective 3D reconstruction method which could be used on a mobile device, if tweaked, is LSD-SLAM. This thesis uses the LSD-SLAM method developed for global shutter cameras and incorporates additional methods developed allow the usage of rolling shutter data.The developed method is evaluated by calculating numbers of failed 3D reconstructions before a successful one is obtained when using rolling shutter data.The result is a method which improves this metric with about 70\% compared to the unedited LSD-SLAM method.

Three-dimensional (3D) structural determination of biological macromolecules is not only critical to understanding their mechanisms, but also has practical applications. Combining the high resolution imaging of transmission electron microscopy (TEM) and efficient computer processing, protein structures in solution or in two-dimensional (2D) crystals can be determined. The lipid monolayer technique uses the high affinity binding of 6His-tagged proteins to a Ni-nitrilotriacetic (NTA) lipid to create high local protein concentrations, which facilitates 2D crystal formation. In this study, several proteins have been crystallized using this technique, including the SARS virus Nsp15 endonuclease and the human Toll-like receptor (TLR) 3 extracellular domain (ECD). Single particle analysis can determine protein structures in solution without the need for crystals. 3D structures of several protein complexes had been solved by the single particle method, including IniA from Mycobacterium tuberculosis, Nsp15 and TLR3 ECD. Determining the structures of these proteins is an important step toward understanding pathogenic microbes and our immune system.

In this thesis, we presented two case studies on vision-based moving objects measurement. In the first case, we used a monocular camera to perform ego-motion estimation for a robot in an urban area. We developed the algorithm based on vertical line features such as vertical edges of buildings and poles in an urban area, because vertical lines are easy to be extracted, insensitive to lighting conditions/shadows, and sensitive to camera/robot movements on the ground plane. We derived an incremental estimation algorithm based on the vertical line pairs. We analyzed how errors are introduced and propagated in the continuous estimation process by deriving the closed form representation of covariance matrix. Then, we formulated the minimum variance ego-motion estimation problem into a convex optimization problem, and solved the problem with the interior-point method. The algorithm was extensively tested in physical experiments and compared with two popular methods. Our estimation results consistently outperformed the two counterparts in robustness, speed, and accuracy. In the second case, we used a camera-mirror system to measure the swimming motion of a live fish and the extracted motion data was used to drive animation of fish behavior. The camera-mirror system captured three orthogonal views of the fish. We also built a virtual fish model to assist the measurement of the real fish. The fish model has a four-link spinal cord and meshes attached to the spinal cord. We projected the fish model into three orthogonal views and matched the projected views with the real views captured by the camera. Then, we maximized the overlapping area of the fish in the projected views and the real views. The maximization result gave us the position, orientation, and body bending angle for the fish model that was used for the fish movement measurement. Part of this algorithm is still under construction and will be updated in the future.

Three-dimensional (3D) structural determination of biological macromolecules is not only critical to understanding their mechanisms, but also has practical applications. Combining the high resolution imaging of transmission electron microscopy (TEM) and efficient computer processing, protein structures in solution or in two-dimensional (2D) crystals can be determined. The lipid monolayer technique uses the high affinity binding of 6His-tagged proteins to a Ni-nitrilotriacetic (NTA) lipid to create high local protein concentrations, which facilitates 2D crystal formation. In this study, several proteins have been crystallized using this technique, including the SARS virus Nsp15 endonuclease and the human Toll-like receptor (TLR) 3 extracellular domain (ECD). Single particle analysis can determine protein structures in solution without the need for crystals. 3D structures of several protein complexes had been solved by the single particle method, including IniA from Mycobacterium tuberculosis, Nsp15 and TLR3 ECD. Determining the structures of these proteins is an important step toward understanding pathogenic microbes and our immune system.