In this paper, we propose a new method for underwater mine detection. This detection strategy is based on the use of the Adaboost algorithm with a Polynomial Image Decomposition (PID). PID splits a given image into two components the geometrical component (cartoon) and the textural one (small scale). This decomposition is based on the use of a polynomial transform. The use of PID reduces the noise and turbidity of underwater images, which results a consequent improvements on the visibility of underwater objects. As a result, our detector achieves a high detection rate and good efficiency. It also shows better performance against the use of a simple adaboost algorithm for underwater mine detection.
CITATION STYLE
El Moubtahij, R., Merad, D., Damoisaux, J. L., & Drap, P. (2017). Mine detection based on adaboost and polynomial image decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10484 LNCS, pp. 660–670). Springer Verlag. https://doi.org/10.1007/978-3-319-68560-1_59
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