Person:

Xiong, Ying

Analyzing images to infer physical scene properties is a fundamental task in computer vision. It is by nature an ill-posed inverse problem, because imaging is a complicated, information-lossy physical and measurement process that cannot be deterministically inverted. This dissertation presents theory and algorithms for handling ambiguities in a variety of low-level vision problems. They are based on two key ideas: (1) explicitly modeling and reporting uncertainties are beneficial to visual inference; and (2) using local models can significantly reduce ambiguities that would exist in pixelwise analysis.

In the first part of the dissertation, we study the color measurement pipeline of consumer digital cameras, and consider the inherent uncertainty of undoing the effects of tone-mapping. We introduce statistical models for this uncertainty and algorithms for fitting it to given cameras or imaging pipelines. Once fit, the model provides for each tone-mapped color a probability distribution over linear scene colors that could have induced it, which is demonstrated to be useful for a number of downstream inference applications.

In the second part of the dissertation, we study the pixelwise ambiguities in physics-based visual inference and present theory and algorithms for employing local models to eliminate or reduce these ambiguities. In shape from shading, we perform mathematical analysis showing that when restricted with quadratic local models, the shape and lighting ambiguities will be reduced to a small finite number of choices as opposed to otherwise continuous manifolds. We propose a framework for surface reconstruction by enforcing consensus on the local regions, which is later enhanced and extended to be applicable to a variety of other visual inference tasks.