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https://hdl.handle.net/20.500.14279/19169
Τίτλος: | DeepCaustics: Classification and Removal of Caustics From Underwater Imagery | Συγγραφείς: | Forbes, Timothy Goldsmith, Mark Mudur, Sudhir Poullis, Charalambos |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Ημερομηνία Έκδοσης: | 21-Ιου-2018 | Πηγή: | IEEE Journal of Oceanic Engineering, 2019, Vol. 44, no. 3, pp. 728 - 738 | Volume: | 44 | Issue: | 3 | Start page: | 728 | End page: | 738 | Project: | Advanced VR, iMmersive serious games and Augmented REality as tools to raise awareness and access to European underwater CULTURal heritage | Περιοδικό: | IEEE Journal of Oceanic Engineering | Περίληψη: | Caustics are complex physical phenomena resulting from the projection of light rays being reflected or refracted by a curved surface. In this paper, we address the problem of classifying and removing caustics from images and propose a novel solution based on two convolutional neural networks: SalienceNet and DeepCaustics. Caustics result in changes in illumination that are continuous in nature; therefore, the first network is trained to produce a classification of caustics that is represented as a saliency map of the likelihood of caustics occurring at a pixel. In applications where caustic removal is essential, the second network is trained to generate a caustic-free image. It is extremely hard to generate real ground truth for caustics. We demonstrate how synthetic caustic data can be used for training in such cases, and then transfer the learning to real data. To the best of our knowledge, out of the handful of techniques that have been proposed, this is the first time that the complex problem of caustic removal has been reformulated and addressed as a classification and learning problem. This paper is motivated by the real-world challenges in underwater archaeology. | URI: | https://hdl.handle.net/20.500.14279/19169 | ISSN: | 03649059 | DOI: | 10.1109/JOE.2018.2838939 | Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International | Type: | Article | Affiliation: | Concordia University | Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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