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http://dx.doi.org/10.9728/dcs.2018.19.1.165

Definition and Analysis of Shadow Features for Shadow Detection in Single Natural Image  

Park, Ki Hong (Division of Convergence Computer & Media, Mokwon University)
Lee, Yang Sun (Division of Convergence Computer & Media, Mokwon University)
Publication Information
Journal of Digital Contents Society / v.19, no.1, 2018 , pp. 165-171 More about this Journal
Abstract
Shadow is a physical phenomenon observed in natural scenes and has a negative effect on various image processing systems such as intelligent video surveillance, traffic surveillance and aerial imagery analysis. Therefore, shadow detection should be considered as a preprocessing process in all areas of computer vision. In this paper, we define and analyze various feature elements for shadow detection in a single natural image that does not require a reference image. The shadow elements describe the intensity, chromaticity, illuminant-invariant, color invariance, and entropy image, which indicate the uncertainty of the information. The results show that the chromaticity and illuminant-invariant images are effective for shadow detection. In the future, we will define a fusion map of various shadow feature elements, and continue to study shadow detection that can adapt to various lighting levels, and shadow removal using chromaticity and illuminance invariant images.
Keywords
Shadow feature; Shadow detection; Chromaticity image; Illuminant-invariant image; Color-invariant image;
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Times Cited By KSCI : 2  (Citation Analysis)
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