Semantic-Aware Image Smoothing

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dc.contributor.authorLi, Weihaoen_US
dc.contributor.authorJafari, Omid Hosseinien_US
dc.contributor.authorRother, Carstenen_US
dc.contributor.editorMatthias Hullin and Reinhard Klein and Thomas Schultz and Angela Yaoen_US
dc.date.accessioned2017-09-25T06:55:40Z
dc.date.available2017-09-25T06:55:40Z
dc.date.issued2017
dc.description.abstractStructure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, we present an interesting observation, i.e. high-level semantic image labeling information can provide a meaningful structure prior naturally. Based on this observation, we propose a simple and yet effective method, which we term semantic smoothing, by exploiting the semantic information to accomplish semantically structure-preserving image smoothing. We show that our approach outperforms the state-of-the-art approaches in texture removal by considering the semantic information for structure preservation. Also, we apply our approach to three applications: detail enhancement, edge detection, and image segmentation, and we demonstrate the effectiveness of our semantic smoothing method on these problems.en_US
dc.description.sectionheadersImage Processing
dc.description.seriesinformationVision, Modeling & Visualization
dc.identifier.doi10.2312/vmv.20171271
dc.identifier.isbn978-3-03868-049-9
dc.identifier.pages153-160
dc.identifier.urihttps://doi.org/10.2312/vmv.20171271
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20171271
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.3 [Image Processing and Computer Vision]
dc.subjectEnhancement
dc.subjectSmoothing
dc.titleSemantic-Aware Image Smoothingen_US
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