Simple shadow removal using shadow depth map and illumination-invariant feature
Pages 4487 - 4502
Abstract
Shadows included in images provide useful information for visual scene analysis, but are also factors that negatively affect digital image analysis. Therefore, shadow detection and removal must be considered essential in the preprocessing of the digital image analysis process. In this paper, the shadow region included in the image is detected using an illumination-invariant image whose characteristics do not change even under the influence of various illuminances, and a shadow removal method using the multi-channel gamma correction and a shadow depth map is proposed. In particular, cast shadows include umbra, which is a shadow that is completely obscured by an object that is covered by a light source according to the intensity of light, and penumbra, which is caused by the diffraction effect. In performing gamma correction of these two regions, the shadow was removed by increasing the brightness of the umbra compared to the penumbra region using the shadow depth map generated based on the statistical characteristics of the detected shadow region. As a result of the experiment, it was shown that the shadow removal of the proposed method effectively removes the umbra region in the natural image containing the shadow.
References
[1]
Xu M, Zhu J, Lv P, Zhou B, Tappen MF, and Ji R Learning-based shadow recognition and removal from monochromatic natural images Proc IEEE Trans Image Process 2017 26 12 5811-5824
[2]
Guo R, Dai G, Hoiem D (2011) Single-image shadow detection and removal using paired regions. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), Colorado Springs, CO, USA, pp 2033–2040.
[3]
Zhu J, Samuel KGG, Masood SZ, Tappen MF (2010) Learning to recognize shadows in monochromatic natural images. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR), San Francisco, CA, USA, pp 223–230.
[4]
Tian J, Tang Y (2011) Linearity of each channel pixel values from a surface in and out of shadows and its applications. In: 2011 IEEE conference on proceeding of computer vision and pattern recognition (CVPR), Providence: RI, Colorado Springs, CO, USA, pp 985–992.
[5]
Wei Z, Yao K, Ji X, and Yang M Removing shadow in color images using a combined algorithm Proceeding of Measuring Technology and Mechatronics Automation, Zhangjiajie, China 2009
[6]
Finlayson GD, Hordley SD, Drew MS (2002) Removing shadows from images using Retinex. In: Proceedings of 10th color and imaging conference final program and proceeding, Scottsdale, Arizona, USA, pp 73–79
[7]
Backes AR, Gonçalves WN, Martinez AS, and Bruno OM Texture analysis and classification using deterministic tourist walk Pattern Recogn 2010 43 3 685-694
[8]
Jyothirmai MSV, Srinivas K, and Srinivasa Rao V Enhancing shadow area using RGB color space IOSR J Comput Eng 2012 2 1 24-28
[9]
Korea Herald Corporation. Newspaper Article [Internet]. http://biz.heraldcorp.com/view.php?ud=20160905000941
[10]
Park KH Shadow detection based intensity and cross entropy for effective analysis of satellite image J Adv Navig Technol 2016 20 4 380-385
[11]
Finlayson GD, Hordley SD, and Drew MS Removing shadows from images Proc Eur Conf Comput Vis Lecture Notes Comput Sci 2002 2353 823-836
[12]
Prati A, Mikic I, Trivedi M, and Cucchiara R Detecting moving shadows: algorithms and evaluation IEEE Trans Pattern Anal Mach Intell 2003 25 7 918-923
[13]
Hsieh JH, Hu WF, Chang CJ, and Chen YS Shadow elimination for effective moving object detection by Gaussian shadow modeling Image Vis Comput 2003 21 6 505-516
[14]
Lalonde JF, Efros AA, Narasimhan SG (2010) Detecting ground shadows in outdoor consumer photographs. In: European conference on computer vision (ECCV), lecture notes in computer science, vol 6312, pp 322–335.
[15]
Sun B, Li S (2010) Moving cast shadow detection of vehicle using combined color models. In: 2010 Chinese conference on pattern recognition (CCPR), pp 1–5.
[16]
Park KH, Kim JH, and Kim YH Shadow detection using chromaticity and entropy in colour image Int J Inf Technol Manage 2018 17 1/2 44-50
[17]
Zheng Q, Qiao X, Cao Y, Lau RWH (2019) Distraction-aware shadow detection. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA, pp 5167–5176.
[18]
Park KH and Lee YS Definition and analysis of shadow features for shadow detection in single natural image J Digit Contents Soc 2018 19 1 165-171
[19]
Vincent N and Mathew S Shadow detection: a review of various approaches to enhance image quality Int J Comput Sci Eng 2014 2 4 49-54
[20]
Wikipedia. Planckian locus [Internet]. https://en.wikipedia.org/wiki/Planckian_locus
[21]
Maddern W, Stewart A, McManus C, Upcroft B, Churchill W, Newman P (2014) Illumination invariant imaging: applications in robust vision-based localisation, mapping and classification for autonomous vehicles. In: Proceedings of the visual place recognition in changing environments workshop, IEEE international conference on robotics and automation
[22]
Wikipedia. Lambertian reflectance [Internet]. https://en.wikipedia.org/wiki/Lambertian_reflectance
[23]
Murali S and Govindan VK Shadow detection and removal from a single image using LAB color space Cybern Inf Technol 2013 13 1 95-103
[24]
Deb K and Suny AH Shadow detection and removal based on YCbCr color space J Smart Comput Rev, Korea Acad Ind Cooper Soc 2014 4 1 23-33
[25]
Freitas VLS, Reis BMF, and Tommaselli AMG Automatic shadow detection in aerial and terrestrial images J Bull Geod Sci 2017 23 4 578-590
[26]
Tomasi C and Manduchi R Bilateral filtering for gray and color images Proc IEEE Int Conf Comput Vis Bombay, India 1998
[27]
Wikipedia. rg Chromaticity [internet]. https://en.wikipedia.org/wiki/Rg_chromaticity
[28]
Drew MS, Finlayson GD, Hordley SD (2003) Recovery of chromaticity image free from shadows via illumination invariance. In: IEEE workshop on color and photometric methods in computer vision (ICCV), pp. 32–39
[29]
Otsu N A threshold selection method from gray-level histograms IEEE Trans Syst Man Cybern 1979 9 1 62-66
[30]
Sirmacek B, Unsalan C (2009) Damaged building detection in aerial images using shadow information. In: Proceedings of the 4th international conference on recent advances in space technologies, Istanbul, Turkey, pp 249–252.
[31]
Scott DW Multivariate density estimation: theory, practice and visualization 2015 2 New Jersey Wiley
[32]
Gonzalez RC, Woods RE, and Eddins SL Digital image processing using MATLAB, ch. 2 2004 1 New Jersey Pearson Prentice Hall 66-68
[33]
Criminisi A, Perez P, Toyama K (2003) Object removal by exemplar-based inpainting. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR), Madison, WI, USA, pp 739–743.
[34]
Keller JB Geometrical theory of diffraction J Opt Soc Am 1962 52 2 116-130
[35]
Primack H, Schanz H, Smilansky U, and Ussishkin I Penumbra diffraction in the quantization of dispersing billiards Phys Rev Lett 1996 76 1615-1618
[36]
Eli A and Hagit HO Shadow removal using intensity surfaces and texture anchor points IEEE Trans Pattern Anal Mach Intell 2011 33 6 1202-1216
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
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Kluwer Academic Publishers
United States
Publication History
Published: 01 February 2022
Accepted: 21 August 2021
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