Color image-based shape reconstruction of multi-color objects under general illumination conditions

Humans have the ability to infer the surface reflectance properties and three-dimensional shape of objects from two-dimensional photographs under simple and complex illumination fields. Unfortunately, the reported algorithms in the area of shape reconstruction require a number of simplifying assumptions that result in poor performance in uncontrolled imaging environments. Of all these simplifications, the assumptions of non-constant surface reflectance, globally consistent illumination, and multiple surface views are the most likely to be contradicted in typical environments. In this dissertation, three automatic algorithms for the recovery of surface shape given non-constant reflectance using a single-color image acquired are presented. In addition, a novel method for the identification and removal of shadows from simple scenes is discussed.In existing shape reconstruction algorithms for surfaces of constant reflectance, constraints based on the assumed smoothness of the objects are not explicitly used. Through Explicit incorporation of surface smoothness properties, the algorithms presented in this work are able to overcome the limitations of the previously reported algorithms and accurately estimate shape in the presence of varying reflectance. The three techniques developed for recovering the shape of multi-color surfaces differ in the method through which they exploit the surface smoothness property. They are summarized below:• Surface Recovery using Pre-Segmentation - this algorithm pre-segments the image into distinct color regions and employs smoothness constraints at the color-change boundaries to constrain and recover surface shape. This technique is computationally efficient and works well for images with distinct color regions, but does not perform well in the presence of high-frequency color textures that are difficult to segment.iv• Surface Recovery via Normal Propagation - this approach utilizes local gradient information to propagate a smooth surface solution from points of known orientation. While solution propagation eliminates the need for color-based image segmentation, the quality of the recovered surface can be degraded by high degrees of image noise due to reliance on local information.• Surface Recovery by Global Variational Optimization - this algorithm utilizes a normal gradient smoothness constraint in a non-linear optimization strategy, to iteratively solve for the globally optimal object surface. Because of its global nature, this approach is much less sensitive to noise than the normal propagation is, but requires significantly more computational resources.Results acquired through application of the above algorithms to various synthetic and real image data sets are presented for qualitative evaluation. A quantitative analysis of the algorithms is also discussed for quadratic shapes. The robustness of the three approaches to factors such as segmentation error and random image noise is also explored

Compensation of atmospheric CO2 buildup through engineered chemical shrinkage

Retrieval of background carbon dioxide into regional chemical extractors would counter anthropogenic inputs in a manner friendly to established industries. We demonstrate via atmospheric transport/scaling calculations that for idealized flat removal units, global coverage could be less than two hundred thousand square kilometers. The disrupted area drops to a small fraction of this with engineering into the vertical to bypass laminarity. Fence structures and artificial roughness elements can both be conceived. Sink thermodynamics are analyzed by taking calcium hydroxide as a sample reactant. Energy costs could be minimized at near the endothermicity of binding reversal. In the calcium case the value is 25 kcal mole-1, as against a fuel carbon content of 150 in the same units. Aqueous kinetics are less than favorable for the hydroxide, but misting could counteract slow liquid phase transfer. Properties of superior scrubbers are outlined

Multispectral oximetry of murine tendon microvasculature with inflammation

We report a novel multispectral imaging technique for localised measurement of vascular oxygen saturation (SO2) in vivo. Annular back-illumination is generated using a Schwarzchild-design reflective objective. Analysis of multispectral data is performed using a calibration-free oximetry algorithm. This technique is applied to oximetry in mice to measure SO2 in microvasculature supplying inflamed tendon tissue in the hind leg. Average SO2 for controls was 94.8 ± 7.0 % (N = 6), and 84.0 ± 13.5 % for mice with inflamed tendon tissue (N = 6). We believe this to be the first localised measurement of hypoxia in tendon microvasculature due to inflammation. Quantification of localised SO2 is important for the study of inflammatory diseases such as rheumatoid arthritis, where hypoxia is thought to play a role in pathogenesis