Abstract
Canny edge detection principle and morphological operations have been used for depth edge detection. Several image smoothing filters were proposed for the enhancement of the detection task. However, an image smoothing filter can blur the edges of an image. In this paper, we propose an enhancement of depth edge detection using an edge-preserving filter, bilateral filter. The filter smooths an image and reduces the edge blurring effects across the edge such as halos and phantom. We maintain a canny edge detection principle and incorporate it with morphological properties. The results show that, this method can detect edges of a depth image better than the method without the edge-preserving filter such as gaussian and median blur.
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References
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698
Durand F, Dorsey J (2002) Fast bilateral filtering for the display of high-dynamic-range images. ACM Siggraph 21:257–266
Eum H, Yoon C, Lee H, Park M (2015) Continuous human action recognition using depth-MHI-HOG and a spotter model. Sensors 15(3):5197–5227
Goyal M (2011) Morphological image processing. IJCST 2(4)
Hasan SMA, Ko K (2016) Depth edge detection by image-based smoothing and morphological operations. J Comput Des Eng 3(3):191–197
Moeslund T (2009). Canny edge detection. Laboratory of Computer Vision and Media Technology, Aalborg University, Denmark
Pal C, Chakrabarti A, Ghosh R (2015) A brief survey of recent edge-preserving smoothing algorithms on digital images. arXiv preprint arXiv:1503.07297
Paris S, Kornprobst P, Tumblin J, Durand F (2008) Bilateral filtering: theory and applications. In: Computer graphics and vision
Scharstein D, Hirschmüller H, Kitajima Y, Krathwohl G, Nesic N, Wang X, Westling P (2014) High-resolution stereo datasets with subpixel-accurate ground truth. In: German conference on pattern recognition (GCPR 2014), Münster, Germany, September 2014
Silberman N et al (2012) Indoor segmentation and support inference from RGBD images. In: European conference on computer vision. Springer, Berlin
Soille P, Morphological image analysis, principles and applications. ISBN 3-540-65671-5 (1999), 2nd edition (2003)
Xia L, Chen C-C, Aggarwal JK (2011) Human detection using depth information by Kinect. In: 2011 IEEE Computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE
Acknowledgements
This research is supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2015-0-00378) supervised by the IITP (Institute for Information & communications Technology Promotion). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (GR 2016R1D1A3B03931911).
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Sung, T.L., Lee, H.J. Depth edge detection using edge-preserving filter and morphological operations. Int J Syst Assur Eng Manag 11, 812–817 (2020). https://doi.org/10.1007/s13198-019-00881-y
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DOI: https://doi.org/10.1007/s13198-019-00881-y