Abstract
The height information of flotation foam is crucial in the titanium flotation. Foam height is embedded within foam point clouds. We are the first to introduce point cloud data in flotation analysis. Addressing the spatial distribution characteristics of holes in the point cloud data, we propose a method to complete the point cloud data by filling the foam height image. We propose a dual-mask-based inpainting approach, enabling inpainting of holes with distinct spatial distribution patterns in the original image. Experimental results demonstrate the effectiveness of the proposed image inpainting method for this specific inpainting task.
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
Horn ZC, Auret L, McCoy JT et al (2017) Performance of convolutional neural networks for feature extraction in froth flotation sensing. IFAC-Papers On Line 50(2):13–18
Mehrshad N, Massinaei M (2011) New image-processing algorithm for measurement of bubble size distribution from flotation froth images. Mining, Metall Expl 28(3):146–150
Jam J, Kendrick C, Walker K et al (2021) A comprehensive review of past and present image inpainting methods. Comput Vis Image Underst 203:103147
Wei C, Tingzhu S, Fangming B et al (2019) Overview of digital image restoration. J New Media 1(1):Article ID 35
Guillemot C, Le Meur O (2021) Image inpainting: overview and recent advances. Computer Vision Image Understand. IEEE Signal Processing Magaz 31(1):127–144; 203:103147
LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551
Goodfellow I, Pouget-Abadie J, Mirza M et al (2020) Generative adversarial networks. Commun ACM 63(11):139–144
Jassim FA (2013) Image inpainting by Kriging interpolation technique. ar**v:1306.0139
Karaca E, Tunga MA (2018) An interpolation-based texture and pattern preserving algorithm for inpainting color images. Expert Syst Appl 91:223–234
Ding D, Ram S, RodrÃguez JJ (2018) Image inpainting using nonlocal texture matching and nonlinear filtering. IEEE Trans Image Process 28(4):1705–1719
Hao L, Shifeng C (2016) Strong edge-aware depth image completion with multi-direction filtering. J Integrat Tech 5(6):36–44
Liu G, Reda FA, Shih K et al (2018) Image inpainting for irregular holes using partial convolutions. In: European Conference on Computer Vision (ECCV) 2018.Computer Vision, vol 15, pp 89–105
Pathak D, Krahenbuhl P, Donahue J et al (2016) Context encoders; feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2536–2544
Yeh RA, Chen C, Lim TY et al (2017) Image inpainting with deep generative models. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Press, New York, pp 5485–5489
Acknowledgements
This work is supported by the National Key Research and Development Program under Grant 2020YFB1708800, Guangdong Key Research and Development Program under Grant 2020B0101130007, Fundamental Research Funds for Central Universities under Grant FRF-MP-20-37, Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110577, and China Postdoctoral Science Foundation under Grant 2021M700385.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lu, Y. et al. (2024). A Novel Point Cloud Completion Method Based on Height Map and Dual-Mask Images. In: Wang, W., Mu, J., Liu, X., Na, Z.N. (eds) Artificial Intelligence in China. AIC 2023. Lecture Notes in Electrical Engineering, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-99-7545-7_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-7545-7_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7544-0
Online ISBN: 978-981-99-7545-7
eBook Packages: Computer ScienceComputer Science (R0)