Real-time Recognition of Shadow from Deep Edge Detection
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http://id.loc.gov/authorities/names/n79058482
Degree Level
Master's
Degree
Master of Science
Department
Department of Computing Science
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Abstract
In this work we address the problem of fast shadow detection from single images of natural scenes. Different from traditional methods that employ expensive optimization methods, we propose a fast semantic-aware Convolutional Neural Network learning framework which trains on different kinds of patches, while integrating semantic shadow information. We primarily cluster pixels based on their material similarities, then in addition to considering individual regions separately, we exploit a higher level interactions between the neighbouring regions. We process the shadow edge pixels between the segments, and relate the regions together.
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http://purl.org/coar/resource_type/c_46ec
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This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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en