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http://dx.doi.org/10.9728/dcs.2018.19.9.1787

Linear Regression-based 1D Invariant Image for Shadow Detection and Removal in Single Natural Image  

Park, Ki-Hong (Division of Convergence Computer & Media, Mokwon University)
Publication Information
Journal of Digital Contents Society / v.19, no.9, 2018 , pp. 1787-1793 More about this Journal
Abstract
Shadow is a common phenomenon observed in natural scenes, but it has a negative influence on image analysis such as object recognition, feature detection and scene analysis. Therefore, the process of detecting and removing shadows included in digital images must be considered as a pre-processing process of image analysis. In this paper, the existing methods for acquiring 1D invariant images, one of the feature elements for detecting and removing shadows contained in a single natural image, are described, and a method for obtaining 1D invariant images based on linear regression has been proposed. The proposed method calculates the log of the band-ratio between each channel of the RGB color image, and obtains the grayscale image line by linear regression. The final 1D invariant images were obtained by projecting the log image of the band-ratio onto the estimated grayscale image line. Experimental results show that the proposed method has lower computational complexity than the existing projection method using entropy minimization, and shadow detection and removal based on 1D invariant images are performed effectively.
Keywords
Invariant image; Illuminant-invariant Image; Linear regression; Shadow detection; Shadow removal;
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Times Cited By KSCI : 1  (Citation Analysis)
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