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 like shadows. Unfortunately, most of the reported algorithms in the area of shape reconstruction require a number of simplifying assumptions that result in poor performance in uncontrolled imaging environments. In this dissertation, automatic algorithms which are able to recover surface shape under realistic illumination and surface reflectance conditions from single photographs are presented.; The techniques developed are: pre-segmentation which 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 fast 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. The second is the normal propagation which utilizes a smoothness-constrained propagation method without pre-segmentation. The technique works better than the previous one but is susceptible to high degrees of image noise. The third, is the variational , which utilizes a global smoothness constraint to iteratively solve for the optimal object surface. It is robust and withstands the influence of random image noise. In addition to these algorithms for surface shape estimation given multi-color objects, a novel method for the identification and removal of shadows from simple scenes is discussed.; 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.