Various Image Based Rendering (IBR) techniques have been proposed to reconstruct scenes from its images. Voxel-based IBR algorithms reconstruct Lambertian scenes well, but fail for specular objects due to limitations of their consistency checks. We show that the conventional consistency techniques fail due to the large variation in reflected color of the surface for different viewing positions. We present a new consistency approach that can predict this variation in color and reconstruct specular objects present in the scene. We also present an evaluation of our technique by comparing it with three other consistency methods.

<div>Plant Phenotyping is a set of methodologies for measuring and analyzing characteristic traits of a plant. While traditional plant phenotyping techniques are labor-intensive and destructive, modern imaging technologies have provided faster, non-invasive, and more cost-effective capabilities for plant phenotyping. Among different image-based phenotyping platforms, I focus on phenotyping with image data captured by Unmanned Aerial Vehicle (UAV) and ground vehicles. The crop plant used in my study is sorghum [Sorghum bicolor (L.) Moench]. In this thesis, I present multiple methods to estimate plot-level and plant-level plant traits from data collected by various platforms, including UAV and ground vehicles. I propose an image plant phenotyping system that provides end-to-end RGB data analysis for plant scientists. I describe a plant segmentation method using HSV color information. I introduce two methods to locate the center of the plants using Multiple Instance Learning (MIL) and Convolutional Neural Networks (CNN). I present three methods to segment individual leaves by shape-based approaches in both Cartesian coordinates and Polar coordinates. I propose a method to estimate leaf length and width for overhead leaf images. I describe a method to estimate leaf angle from data collected by a modified wheel-based sprayer with a sensor boom vehicle, Phenorover. Methods are tested and verified on image data collected by UAV and ground vehicle platforms in sorghum fields in West Lafayette, Indiana, USA. Estimated phenotypic traits include plant locations, the number of plants per plot, leaf area, canopy cover, Leaf Area Index (LAI), leaf count, leaf angle, leaf length, and leaf width.</div>

Patient-specific multi-physics simulations have the potential to improve the diagnosis, treatment, and scientific inquiry of heart valve dynamics. It has been shown that the flow characteristics within the left ventricle are important to correctly capture the aortic and mitral valve motion and corresponding fluid dynamics, motivating the use of patient-specific imaging to describe the aortic and mitral valve geometries as well as the motion of the left ventricle (LV). The LV position can be captured at several time points in the cardiac cycle, such that its motion can be prescribed a priori as a Dirichlet boundary condition during a simulation. Valve leaflet motion, however, should be computed from soft-tissue models and incorporated using fully-coupled Fluid Structure Interaction (FSI) algorithms. While FSI simulations have in part or wholly been achieved by multiple groups, to date, no high-throughput models have been developed, which are needed for use in a clinical environment. This project seeks to enable patient-derived moving LV boundary conditions, and has been developed for use with a previously developed immersed boundary, fixed Cartesian grid FSI framework. One challenge in specifying LV motion from medical images stems from the low temporal resolution available. Typical imaging modalities contain only tens of images during the cardiac cycle to describe the change in position of the left ventricle. This temporal resolution is significantly lower than the time resolution needed to capture fluid dynamics of a highly deforming heart valve, and thus an approach to describe intermediate positions of the LV is necessary. Here, we propose a primarily Eulerian means of representing LV displacement. This is a natural extension, since an Eulerian framework is employed in the CFD model to describe the large displacement of the heart valve leaflets. This approach to using Eulerian interface representation is accomplished by applying “morphing” techniques commonly used in the field of computer graphics. For the approach developed in the current work, morphing is adapted to the unique characteristics of a Cartesian grid flow solver which presents challenges of adaptive mesh refinement, narrow band approach, parallel domain decomposition, and the need to supply a local surface velocity to the flow solver that describes both normal and tangential motion. This is accomplished by first generating a skeleton from the Eulerian interface representation, and deforming the skeleton between image frames to determine bulk displacement. After supplying bulk displacement, local displacement is determined using the Eulerian fields. The skeletons are also utilized to automate the simulation setup to track the locations upstream and downstream where the system inflow/outflow boundary conditions are to be applied, which in the current approach, are not limited to Cartesian domain boundaries.

<p>Human viewers are extremely sensitive to the appearanceof peoples faces, which makes the rendering of realistic human faces a challenging problem. Techniques for doing this have continuously been invented and evolved since more than thirty years. </p><p>This thesis makes use of recent methods within the area of image based rendering, namely the acquisition of reflectance fields from human faces. The reflectance fields are used to synthesize and realistically render models of human faces. </p><p>A shape from shading technique, assuming that human skin adheres to the Phong model, has been used to estimate surface normals. Belief propagation in graphs has then been used to enforce integrability before reconstructing the surfaces. Finally, the additivity of light has been used to realistically render the models. </p><p>The resulting models closely resemble the subjects from which they were created, and can realistically be rendered from novel directions in any illumination environment.</p>

<p>A new approach for object replacement in 3D space is presented. Introducing a technique that replaces the older two dimensional (2D) based facial replacement method performed by compositing artist in motion picture productions and video commercial industry.</p><p>This method uses 4 digital video cameras filming an actor from 360 degrees, the cameras are placed with 90 degrees in between, the video footage acquired is then used to produce a 3D video texture consisting of video segments taken from different angles representing the object from 3D point of view.</p><p>The video texture is then applied to a 3D modelled head matching the geometry of the original object.</p><p>Offering the freedom of showing the object from any point of view from 3D space, which is not possible using the current two dimensional method where the actormust at all time face the camera.</p><p>The method is described in details with images showing every stage of the process.</p><p>Results are presented as still frames taken from the final video footage and as a video file demonstrating them.</p>

Previous research from environmental psychology shows that human well-being suffers in windowless environments in many ways and a window view of nature is psychologically and physiologically beneficial to humans. Current window substitutes, still images and video, lack three dimensional properties necessary for a realistic viewing experience – primarily motion parallax. We present a new system using a head-coupled display and image-based rendering to simulate a photorealistic artificial window view of nature with motion parallax. Evaluation data obtained from human subjects suggest that the system prototype is a better window substitute than a static image and has significantly more positive effects on observers’ moods. The test subjects judged the system prototype as a good simulation of, and acceptable replacement for, a real window, and accorded it much higher ratings for realism and preference than a static image.

Human viewers are extremely sensitive to the appearanceof peoples faces, which makes the rendering of realistic human faces a challenging problem. Techniques for doing this have continuously been invented and evolved since more than thirty years. This thesis makes use of recent methods within the area of image based rendering, namely the acquisition of reflectance fields from human faces. The reflectance fields are used to synthesize and realistically render models of human faces. A shape from shading technique, assuming that human skin adheres to the Phong model, has been used to estimate surface normals. Belief propagation in graphs has then been used to enforce integrability before reconstructing the surfaces. Finally, the additivity of light has been used to realistically render the models. The resulting models closely resemble the subjects from which they were created, and can realistically be rendered from novel directions in any illumination environment.

Generating in-between images from multiple views of a scene is a crucial task for both computer vision and computer graphics fields. Photorealistic rendering, 3DTV and robot navigation are some of many applications which benefit from arbitrary view synthesis, if it is achieved in real-time. Most modern commodity computer architectures include programmable processing chips, called Graphics Processing Units (GPU), which are specialized in rendering computer generated images. These devices excel in achieving high computation power by processing arrays of data in parallel, which make them ideal for real-time computer vision applications. This thesis focuses on an arbitrary view rendering algorithm by using two high resolution color cameras along with a single low resolution time-of-flight depth camera and matching the programming paradigms of the GPUs to achieve real-time processing rates. Proposed method is divided into two stages. Depth estimation through fusion of stereo vision and time-of-flight measurements forms the data acquisition stage and second stage is intermediate view rendering from 3D representations of scenes. Ideas presented are examined in a common experimental framework and practical results attained are put forward. Based on the experimental results, it could be concluded that it is possible to realize content production and display stages of a free-viewpoint system in real-time by using only low cost commodity computing devices.

Previous research from environmental psychology shows that human well-being suffers in windowless environments in many ways and a window view of nature is psychologically and physiologically beneficial to humans. Current window substitutes, still images and video, lack three dimensional properties necessary for a realistic viewing experience – primarily motion parallax. We present a new system using a head-coupled display and image-based rendering to simulate a photorealistic artificial window view of nature with motion parallax. Evaluation data obtained from human subjects suggest that the system prototype is a better window substitute than a static image and has significantly more positive effects on observers’ moods. The test subjects judged the system prototype as a good simulation of, and acceptable replacement for, a real window, and accorded it much higher ratings for realism and preference than a static image.

<p>In the research of developing arbitrary and unique virtual views from a real- world scene, a prototype of an interactive relief texture mapping system capable of processing video using dynamic image-based rendering, is developed in this master thesis. The process of deriving depth from recorded video using binocular stereopsis is presented, together with how the depth information is adjusted to be able to manipulate the orientation of the original scene. When the scene depth is known, the recorded organic and dynamic objects can be seen from viewpoints not available in the original video.</p>