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Paper TuC01.5

Hung, Calvin (University of Sydney), Bryson, Mitch (University of Sydney), Sukkarieh, Salah (University of Sydney)

"ShadowCut" - an Unsupervised Object Segmentation Algorithm for Aerial Robotic Surveillance Applications

Scheduled for presentation during the Regular Session "Autonomy and Vision for UAVs" (TuC01), Tuesday, May 15, 2012, 15:30−15:45, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords Aerial Robotics, Field Robots, Recognition

Abstract

This paper introduces an unsupervised graph cut based object segmentation algorithm, ShadowCut, for robotic aerial surveillance applications. By exploiting the spatial setting of the aerial imagery, ShadowCut algorithm differs from state-of-the-art object segmentation algorithms by not requiring a large number of labelled training data set, nor constant user interaction. In this paper it is shown that, by combining robotic navigation data and a shadow model, it is possible to provide these seed labels with a probabilistic sampling model for object segmentation in aerial imagery. Experiments were performed on aerial data sets consisting of data collected in outback Australia with an aerial robotic platform during an ecological surveillance mission, and aerial images with various natural targets from Google Earth. The segmentation results from the unsupervised ShadowCut algorithm are shown to be comparable with those from supervised graph cut algorithms.

 

 

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