Uncertain Reasoning and Learning for 3-D Object Detection and Description

ZuWhan Kim


Abstract

Acquiring 3-D object description from a single or multiple images has been a key goal of computer vision. Building detection and description is a practical application for this problem. In this proposal, I follow a hypothesize and verify paradigm, where decision making at various stages is important. Most of the previous work has been focused on feature grouping, and the decisions were usually made by ad hoc operators. In this proposal, I show experiments of applying uncertain reasoning and learning for hypothesis verification. First experiment with monocular building detection and description system verifies the idea that the uncertain reasoning and learning will bring better result with smaller efforts for tuning the parameters. Based on these results I apply Bayesian inference to the multi-view system, where the number of evidence inputs varies according to the number of views. I propose a dynamic Bayesian network to deal with this situation. I also introduce a novel way of designing a Bayesian network structure. In the experimental results, the proposed method shows a superior performance to that of a state-of-art classifier. Finally, I propose a future a research for the multi-layered polygonal rooftop building detection and description. I show some preliminary results and discuss remaining scientific challenges.

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Maintained by Alexandre R.J. FRANÇOIS