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Embodied object recognition

University of British Columbia

The ability to localize and categorize objects via imagery is central to many potential applications, including autonomous vehicles, mobile robotics, and surveillance. In this thesis we employ a probabilistic approach to show how utilizing multiple images of the same scene can improve detection. We cast the task of object detection as finding the set of objects that maximize the posterior probability given a model of the categories and a prior for their spatial arrangements. We first present an approach to detection that leverages depth data from binocular stereo by factoring classification into two terms: an independent appearance-based object classifier, and a term for the 3D shape. We overcome the missing data and the limited fidelity of stereo by focusing on the size of the object and the presence of discontinuities. We go on to demonstrate that even with off-the-shelf stereo algorithms we can significantly improve detection on two household objects, mugs and shoes, in the presence of significant background clutter and textural variation. We also present a novel method for object detection, both in 2D and in 3D, from multiple images with known extrinsic camera parameters. We show that by also inferring the 3D position of the objects we can improve object detection by incorporating size priors and reasoning about the 3D geometry of a scene. We also show that integrating information across multiple viewpoints allows us to boost weak classification responses, overcome occlusion, and reduce false positives. We demonstrate the efficacy of our approach, over single viewpoint detection, on a dataset containing mugs, bottles, bowls, and shoes in a variety of challenging scenarios.

Text

2012-06-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/42481

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/