Date of Award

Winter 12-2015

Embargo Period

4-7-2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Robotics Institute

Advisor(s)

David Wettergreen

Abstract

The real world is a rich environment, fraught with complexity. To be robust in this complex environment, computer Vision algorithms that operate in Unstructured Environments (VUE) tend to use large amounts of data or complex modeling. Unfortunately, these algorithms also require significant computational resources. In this thesis, we examine a visual utility framework that we show is used regularly in an ad hoc manner. This framework uses visual utility estimators to speed up VUE algorithms with minimal performance degradation by focusing those higher level algorithms on the most relevant imagery. We formally define this framework, show that it has a submodular structure and discover under what conditions using it is valuable in practice. We find that visual utility approaches are most effective when using fast, task specific visual utility estimators and the VUE task is computationally expensive, We also introduce SCATAT, a cascade building algorithm. SCATAT takes advantage of the submodular structure of the visual utility framework in order to build a near optimal cascade that trades o task performance and processing requirements explicitly. We then validate this algorithm in an experimental case study on object detection. Finally, in theoretical case studies, we prove that two seminal cascade algorithms are special cases of our visual utility framework. We show they optimize a submodular visual utility function, explaining their high observed performance in practice.

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