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Photo editing software allows digital images to be blurred, warped or re-colored at the touch of a button. However, it is not currently possible to change the material appearance of an object except by painstakingly painting over the appropriate pixels. Here we present a set of methods for automatically replacing one material with another, completely different material, starting with only a single high dynamic range image, and an alpha matte specifying the object. Our approach exploits the fact that human vision is surprisingly tolerant of certain (sometimes enormous) physical inaccuracies. Thus, it may be possible to produce a visually compelling illusion of material transformations, without fully reconstructing the lighting or geometry. We employ a range of algorithms depending on the target material. First, an approximate depth map is derived from the image intensities using bilateral filters. The resulting surface normals are then used to map data onto the surface of the object to specify its material appearance. To create transparent or translucent materials, the mapped data are derived from the object's background. To create textured materials, the mapped data are a texture map. The surface normals can also be used to apply arbitrary bidirectional reflectance distribution functions to the surface, allowing us to simulate a wide range of materials. To facilitate the process of material editing, we generate the HDR image with a novel algorithm, that is robust against noise in individual exposures. This ensures that any noise, which would possibly have affected the shape recovery of the objects adversely, will be removed. We also present an algorithm to automatically generate alpha mattes. This algorithm requires as input two images--one where the object is in focus, and one where the background is in focus--and then automatically produces an approximate matte, indicating which pixels belong to the object. The result is then improved by a second algorithm to generate an accurate alpha matte, which can be given as input to our material editing techniques.

High dynamic range (HDR) imaging is a rapidly growing field in computer graphics and image processing. It allows capture, storage, processing, and display of photographic information within a scene-referred framework. The HDR imaging pipeline consists of the major steps an HDR image is expected to go through from capture to display. It involves various techniques to create HDR images, pixel encodings and file formats for storage, tone mapping for display on conventional display devices and direct display on HDR capable screens. Each of these stages have important open problems, which need to be addressed for a smoother transition to an HDR imaging pipeline. We addressed some of these important problems such as noise reduction in HDR imagery, preservation of color appearance, validation of tone mapping operators, and image display on HDR monitors. The aim of this thesis is thus, to present our findings and describe the research we have conducted within the framework of optimizing the HDR imaging pipeline.

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No description available.

No description available.

No description available.

<p>Microwave imaging is a promising new imaging modality under research for breast cancer detection. This technique images/reconstructs the internal dielectric composition of the breasts and relies on the contrast between the dielectric properties of malignant tissues and healthy tissues to pinpoint the abnormality. Over the years, new imaging algorithms were proposed and many imaging systems were developed in accordance. However, none of the proposed systems has made it to the market.</p> <p>In this thesis, a prototype planar raster-scanning system for near-field microwave imaging is presented. This system measures the scattering parameters while scanning a 2-D plane over the imaged object (phantom) in a raster pattern. The development of this system aids significantly in our research of microwave imaging for breast cancer detection because it enables us to carry out numerous experiments and to develop and verify new imaging algorithms.</p> <p>Our contribution also lies in conducting a comprehensive study of the dynamic range of the developed system. Each source of noise/uncertainty from the system is identified and studied for the benefits of future improvements.</p> <p>Typical imaging results of phantoms with different dielectric properties are also provided to showcase the performance of the developed system.</p> / Master of Applied Science (MASc)

High speed video has been a significant tool for unraveling the quantitative and qualitative assessment of phenomena that is too fast to readily observe. It was first used in 1852 by William Henry Fox Talbot to settle a dispute with reference to the synchronous position of a horse's hooves while galloping. Since that time private industry, government, and enthusiasts have been measuring dynamic scenarios with high speed video. One challenge that faces the high speed video community is the dynamic range of the sensors. The dynamic range of the sensor is constrained to the bit depth of the analog to digital converter, the deep well capacity of the sensor site, and baseline noise. A typical high speed camera can span a 60 dB dynamic range, 1000:1, natively. More recently the dynamic range has been extended to about 80 dB utilizing different pixel acquisition methods. In this dissertation a method to extend the dynamic range will be presented and demonstrated to extend the dynamic range of a high speed camera system to over 170 dB, about 31,000,000:1. The proposed formation methodology is adaptable to any camera combination, and almost any needed dynamic range. The dramatic increase in the dynamic range is made possible through an adaptation of the current high dynamic range image formation methodologies. Due to the high cost of a high speed camera, a minimum number of cameras are desired to form a high dynamic range high speed video system. With a reduced number of cameras spanning a significant range, the errors on the formation process compound significantly relative to a normal high dynamic range image. The increase in uncertainty is created from the lack of relevant correlated information for final image formation, necessitating the development of a new formation methodology. In the proceeding text the problem statement and background information will be reviewed in depth. The development of a new weighting function, stochastic image formation process, tone map methodology, and optimized multi camera design will be presented. The proposed methodologies' effectiveness will be compared to current methods throughout the text and a final demonstration will be presented. / Ph. D.