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Proceedings Paper

Visual front-end for underwater scene change detection and environment monitoring by the autonomous drone
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Paper Abstract

Autonomous underwater drone operation requires on-line analysis of signals coming from various sensors. In this paper we focus on design of the visual front-end of an underwater drone which is optimized for abrupt signal change detection for help in maneuvering and underwater object search operations. The proposed method relies on tensor space comparison with the chordal kernel function. This kernel measures a distance expressed as principal angles on Grassman manifolds of unfolded tensors. Although tested on color videos, the method can be scaled to accept more signal types in the input tensors. Experiments show promising results.

Paper Details

Date Published: 14 May 2019
PDF: 9 pages
Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 109960U (14 May 2019); doi: 10.1117/12.2519390
Show Author Affiliations
Boguslaw Cyganek, AGH Univ. of Science and Technology (Poland)
Bogdan Smolka, Silesian Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10996:
Real-Time Image Processing and Deep Learning 2019
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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