Abstract
Economical RGB-D cameras such as Kinect can produce both RGB and depth (RGB-D) images in real-time. The accuracy of various RGB-D related applications suffers from depth image noise. This paper proposes a solution to the problem by estimating depth edges that correspond to the object boundaries and using them as priors in the hole filling process. This method exhibits quantitative and qualitative improvements over the current state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)
Lu, J., Shi, K., Min, D., Lin, L., Do, M.N.: Cross-based local multipoint filtering. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Xu, L., Au, O.C., Sun, W., Li, Y., Li, J.: Hybrid plane fitting for depth estimation. In: Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC) (2012)
Matsumoto, K., De Sorbier, F., Saito, H.: Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time. In: SciTePress (2015)
Yang, J., Ye, X., Li, K., Hou, C.: Depth recovery using an adaptive color-guided auto-regressive model. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 158–171. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33715-4_12
Camplani, M., Salgado, L.: Efficient spatio-temporal hole filling strategy for Kinect depth maps. In: International Society for Optics and Photonics, IS&T SPIE Electronic Imaging (2012)
Liu, J., Gong, X., Liu, J.: Guided inpainting and filtering for Kinect depth maps. In: 2012 21st International Conference on Pattern Recognition (ICPR) (2012)
Wang, Z., Hu, J., Wang, S., Lu, T.: Trilateral constrained sparse representation for Kinect depth hole filling. Pattern Recogn. Lett. 65, 95–102 (2015)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: Computer Vision and Pattern Recognition (2007)
Riemens, O., Gangwal, O., Barenbrug, B., Berretty, R.-P.: Multistep joint bilateral depth upsampling. In: International Society for Optics and Photonics, IS&T SPIE Electronic Imaging (2009)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Jaiswal, M.S., Wang, YY., Sun, MT. (2017). Object Boundary Based Denoising for Depth Images. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-59876-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59875-8
Online ISBN: 978-3-319-59876-5
eBook Packages: Computer ScienceComputer Science (R0)