Date of Award


Embargo Period


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Robotics Institute


Srinivasa G. Narasimhan


Nonrigid deformation modeling and estimation from images is a technically challenging task due to its nonlinear, nonconvex and high-dimensional nature. Traditional optimization procedures often rely on good initializations and give locally optimal solutions. On the other hand, learning-based methods that directly model the relationship between deformed images and their parameters either cannot handle complicated forms of mapping, or suffer from the Nyquist Limit and the curse of dimensionality due to high degrees of freedom in the deformation space. In particular, to achieve a worst-case guarantee of ∈ error for a deformation with d degrees of freedom, the sample complexity required is O(1/∈d).

In this thesis, a generative model for deformation is established and analyzed using a unified theoretical framework. Based on the framework, three algorithms, Data-Driven Descent, Top-down and Bottom-up Hierarchical Models, are designed and constructed to solve the generative model. Under Lipschitz conditions that rule out unsolvable cases (e.g., deformation of a blank image), all algorithms achieve globally optimal solutions to the specific generative model. The sample complexity of these methods is substantially lower than that of learning-based approaches, which are agnostic to deformation modeling.

To achieve global optimality guarantees with lower sample complexity, the structure
embedded in the deformation model is exploited. In particular, Data-driven Descent
relates two deformed images that are far away in the parameter space by compositional
structures of deformation and reduce the sample complexity to O(Cd log 1/∈).
Top-down Hierarchical Model factorizes the local deformation into patches once the
global deformation has been estimated approximately and further reduce the sample
complexity to O(Cd/1+C2 log 1/∈). Finally, the Bottom-up Hierarchical Model builds
representations that are invariant to local deformation. With the representations, the
global deformation can be estimated independently of local deformation, reducing
the sample complexity to O((C/∈)d0) (d0 ≪ d). From the analysis, this thesis shows
the connections between approaches that are traditionally considered to be of very
different nature. New theoretical conjectures on approaches like Deep Learning, are
also provided.

practice, broad applications of the proposed approaches have also been demonstrated to estimate water distortion, air turbulence, cloth deformation and human pose with state-of-the-art results. Some approaches even achieve near real-time performance. Finally, application-dependent physics-based models are built with good performance in document rectification and scene depth recovery in turbulent media.



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