| A physical model-based approach to detecting sky in photographic images. | |
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MedLine Citation:
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PMID: 18244624 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Sky is among the most important subject matter frequently seen in photographic images. We propose a model-based approach consisting of color classification, region extraction, and physics-motivated sky signature validation. First, the color classification is performed by a multilayer backpropagation neural network trained in a bootstrapping fashion to generate a belief map of sky color. Next, the region extraction algorithm automatically determines an appropriate threshold for the sky color belief map and extracts connected components. Finally, the sky signature validation algorithm determines the orientation of a candidate sky region, classifies one-dimensional (1-D) traces within the region based on a physics-motivated model, and computes the sky belief of the region by the percentage of traces that fit the physics-based sky trace model. A small-scale, yet rigorous test has been conducted to evaluate the algorithm performance. With approximately half of the images containing blue sky regions, the detection rate is 96% with a false positive rate of 2% on a per image basis. |
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Authors:
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Jiebo Luo; S P Etz |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Volume: 11 ISSN: 1057-7149 ISO Abbreviation: IEEE Trans Image Process Publication Date: 2002 |
Date Detail:
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Created Date: 2008-02-04 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9886191 Medline TA: IEEE Trans Image Process Country: United States |
Other Details:
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Languages: eng Pagination: 201-12 Citation Subset: - |
Affiliation:
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Res. Labs., Eastman Kodak Co., Rochester, NY. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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