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.

This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated.

Bidirectional reflectance is a fundamental radiative property of rough surfaces. Knowledge of the bidirectional reflectance is crucial to the emissivity modeling and heat transfer analysis. This thesis concentrates on the modeling and measurements of the bidirectional reflectance for microrough silicon surfaces and on the validity of a hybrid method in the modeling of the bidirectional reflectance for thin-film coated rough surfaces. The surface topography and the bidirectional reflectance distribution function (BRDF) of the rough side of several silicon wafers have been extensively characterized using an atomic force microscope and a laser scatterometer, respectively. The slope distribution calculated from the surface topographic data deviates from the Gaussian distribution. Both nearly isotropic and strongly anisotropic features are observed in the two-dimensional (2-D) slope distributions and in the measured BRDF for more than one sample. The 2-D slope distribution is used in a geometric-optics based model to predict the BRDF, which agrees reasonably well with the measured values. The side peaks in the slope distribution and the subsidiary peaks in the BRDF for two anisotropic samples are attributed to the formation of {311} planes during chemical etching. The correlation between the 2-D slope distribution and the BRDF has been developed. A boundary integral method is applied to simulate the bidirectional reflectance of thin-film coatings on rough substrates. The roughness of the substrate is one dimensional for simplification. The result is compared to that from a hybrid method which uses the geometric optics approximation to model the roughness effect and the thin-film optics to consider the interference due to the coating. The effects of the film thickness and the substrate roughness on the validity of the hybrid method have been investigated. The validity regime of the hybrid method is established for silicon dioxide films on silicon substrates in the visible wavelength range. The proposed method to characterize the microfacet orientation and to predict the BRDF may be applied to other anisotropic or non-Gaussian rough surfaces. The measured BRDF may be used to model the apparent emissivity of silicon wafers to improve the temperature measurement accuracy in semiconductor manufacturing processes. The developed validity regime for the hybrid method can be beneficial to future research related to the modeling for thin-film coated rough surfaces.

Procedural textures and image textures are commonplace in graphics today, finding uses in such places as animated movies and video games. Unlike image texture maps, procedural textures typically suffer from minification aliasing. I present a method that, given a procedural texture on a surface, automatically creates an anti-aliased version of the procedural texture. The new procedural texture maintains the original textures details, but reduces minification aliasing artifacts. This new algorithm creates an image pyramid similar to MIP-Maps to represent the texture. Whereas a MIP-Map stores per-texel color, however, my texture hierarchy stores weighted sums of reflectance functions, allowing a wider-range of effects to be anti-aliased. The stored reflectance functions are automatically selected based on an analysis of the different functions found over the surface. When the texture is viewed at close range, the original texture is used, but as the texture footprint grows, the algorithm gradually replaces the textures result with an anti-aliased one. This results in faster development time for writing procedural textures as well as higher visual fidelity and faster rendering. With the optional addition of authoring guidelines, the analysis phase can be sped up by as much as two orders of magnitude. Furthermore, I developed a method for handling pre-filtered integration of reflectance functions to anti-alias specular highlights. The normal-centric BRDF (NBRDF) allows for fast evaluation over a range of normals appearing on the surface of an object. The NBRDF is easy to implement on the GPU for real-time results and can be combined with procedural reduction maps for real-time procedural texture minification anti-aliasing.

Near infrared reflectance spectroscopy (NIRS) and geostatistical techniques were used to predict diet quality of sub-Saharan African (SSA) livestock, and to create cokriged estimated diet quality maps for cattle across a landscape. Rations of native vegetation were stall-fed to cattle (Bos indicus), sheep (Ovis aries), and goats (Capra hircus) to generate diet-fecal pair data. Trials were conducted in Ethiopia, Kenya, Uganda, Tanzania, and Ghana. Historical data from Ethiopia, Nigeria, and Niger were included. Diet samples were analyzed for crude protein (CP%), and digestible organic matter (DOM%), while feces were scanned for NIR spectra. NIRS equations were developed from data using modified partial least square (MPLS) regression. Coefficients of determination (R2) of CP for cattle, sheep, and goats were 0.92, 0.95, and 0.97, with corresponding standard errors of calibration (SEC) being 0.90, 0.79, and 0.80, respectively. Standard errors of cross validation (SECV) for CP were 1.12%, 1.08%, and 1.03% for cattle, sheep, and goats, respectively. R2 and SEC values for DOM were 0.88, 0.94, 0.94 and 2.82%, 1.68%, and 2.65%, for cattle, sheep, and goats, respectively. Corresponding SECV values for DOM were 3.26%, 2.07%, and 3.30%, respectively. The statistics reported were within the acceptable limits for NIRS calibrations. The results indicate that dietary CP and DOM of free-ranging SSA livestock can be predicted with the same precision as that of conventional wet chemistry methods. The cattle equation was used to predict cattle fecal samples collected, from February to August 2000, from selected households located within the northern Ghana savanna. The predicted CP% and DOM% were used with Normalized Differential Vegetation Index (NDVI) data, and cokriging technique to create diet quality maps for March and July 2000 for the northern Ghana savanna. Cross validation results indicated a moderate capability of cokriging to estimate predicted CP% for March (r2 = 0.687, SEp = 1.736) and July (r2 = 0.513, SEp = 1.558). Cokriged-estimated DOM value for July was above average (r2 = 0.584, SEp = 3.611), while March DOM% estimation was rather poor (r2 = 0.132, SEp = 3.891). The techniques of cokriging and creation of diet quality maps were moderately successful in this study.

No description available.

This dissertation presents the development and use of a novel optical imaging system capable of monitoring changes in blood flow, oxygenated hemoglobin, deoxygenated hemoglobin, and absolute pO₂ in the brain. There are several imaging modalities capable of monitoring these parameters separately. Laser speckle contrast imaging (LSCI) and multi-spectral reflectance imaging (MSRI) have been used to monitor relative blood flow and hemoglobin changes respectively. Phosphorescence quenching, while not typically used for imaging, is capable of noninvasive measurements of pO₂. Combining these three techniques has led to the development of an imaging system that could ultimately lead to a better understanding of brain physiology. By combining techniques such as LSCI and MSRI, it becomes possible to estimate the cerebral metabolic rate of oxygen (CMRO₂), an important indicator of neuronal function. It is equally important to understand absolute pO₂ levels so that oxygen metabolism can be examined in context. Integrating phosphorescence quenching and a spatial light modulator into the imaging system allowed absolute pO₂ to be simultaneously measured in distinct regions. This new combined system was used to investigate pathophysiological conditions such as cortical spreading depression (CSD) and ischemia. The observed hemodynamic changes associated with these events were largely dictated by baseline oxygen levels and varied significantly in different regions. This finding highlighted the importance of having a system capable of monitoring hemodynamic changes and absolute pO₂ simultaneously while maintaining enough spatial resolution to distinguish the changes in different regions. It was found that animals with low baseline pO₂ were unable to deliver enough oxygen to the brain during events like CSD because of the high metabolic demand. In order for this technique to become more prevalent among researchers, it is essential to make it cost effective and simple to use. This was accomplished by replacing the expensive excitation sources with cheaper light emitting diodes (LEDs) and redesigning the software interface so that it was easier to control the entire device. The final system shows the potential to become a key tool for researchers studying the role of absolute pO₂ and other hemodynamic parameters during pathophysiological conditions such as CSD and ischemia. / text

Spectroscopic second-harmonic generation (SHG) and reflectance-anisotropy spectroscopy (RAS) are used to probe molecular adsorption on clean reconstructed single-domain stepped Si(001) in ultra-high vacuum (UHV). We implement a simplified bond hyperpolarizability model (SBHM) as a common microscopic analysis for SHG and RAS. Three different scenarios are studied: (i) The dissociative adsorption of molecular hydrogen on dangling bonds of D[subscript B] step-edges. (ii) Structural changes to rebonded r-D[subscript B] steps induced by exposure to atomic hydrogen. (iii) The adsorption of cyclopentene on Si(001)(2x1) terrace dimers in a [2+2] cycloaddition pathway. Using the SBHM we develop a new optical fingerprinting method to isolate, identify and monitor individual types of bonds (e.g. dimers, rebonds, dangling bonds, backbonds) and their chemical activity on a single-domain stepped Si(001) surface using nonresonant, but rotationally-anisotropic, second-harmonic generation (RA-SHG). The methods presented here will be applicable to many material systems and allow to track, in-situ and in real-time, the chemical action of adsorbates on surfaces. / text

A novel non-destructive method of grape berry analysis is presented that uses reflected light to predict berry composition. The reflectance spectrum was collected using a diode array spectrometer (350 to 850 nm) over the 2009 and 2010 growing seasons. Partial least squares regression (PLS) and support vector machine regression (SVMR) generated calibrations between reflected light and composition for five berry components, total soluble solids (°Brix), titratable acidity (TA), pH, total phenols, and anthocyanins. Standard methods of analysis for the components were employed and characterized for error. Decomposition of the reflectance data was performed by principal component analysis (PCA) and independent component analysis (ICA). Regression models were constructed using 10x10 fold cross validated PLS and SVM models subject to smoothing, differentiation, and normalization pretreatments. All generated models were validated on the alternate season using two model selection strategies: minimum root mean squared error of prediction (RMSEP), and the "oneSE" heuristic. PCA/ICA decomposition demonstrated consistent features in the long VIS wavelengths and NIR region. The features are consistent across seasons. 2009 was generally more variable, possibly due to cold weather affects. RMSEP and R2 statistics of models indicate that PLS °Brix, pH, and TA models are well predicted for 2009 and 2010. SVM was marginally better. The R2 values of the PLS °Brix, pH, and TA models for 2009 and 2010 respectively were: 0.84, 0.58, 0.56 and: 0.89, 0.81, 0.58. 2010 °Brix models were suitable for rough screening. Optimal pretreatments were SG smoothing and relative normalization. Anthocyanins were well predicted in 2009, R2 0.65, but not in 2010, R2 0.15. Phenols were not well predicted in either year, R2 0.15-0.25. Validation demonstrated that °Brix, pH, and TA models from 2009 transferred to 2010 with fair results, R2 0.70, 0.72, 0.31. Models generated using 2010 reflectance data did not generate models that could predict 2009 data. It is hypothesized that weather events present in 2009 and not in 2010 allowed for a forward calibration transfer, and prevented the reverse calibration transfer. Heuristic selection was superior to minimum RMSEP for transfer, indicating some overfitting in the minimum RMSEP models. The results are demonstrative of a reflectance-composition relationship in the VIS-NIR region for °Brix, pH, and TA requiring additional study and development of further calibrations.

No description available.