Résumé:
Detecting fish in submarine environment is a challenge due to the properties of the water such as light absorption
and scattering. In this work, we present a method for preprocessing images in submarine environment. In the first step, we
model the underwater environment as overlapp of two processes.
The first process is considered as a Poisson distribution, while
the second one is considered as a Gaussian mixture. The resulting distribution is called Poisson-Gaussian mixture (PGM). To
estimate the noise parameters, we propose an iterative algorithm
based on the expectation maximization approach. This allows us
to jointly estimate the scale of the Poisson parameter as well as
the standard deviation and the mean of all Gaussian distributions.
In order to facilitate the detection of objects, to correct the
illumination problem of the scene and to restore the colors,
we integrate a color correction algorithm. Finally, detection and
localization of fish complete the pre-processing in the images.
To obtain medium or small regions, the mean shift algorithm is
used with a reduced threshold. In the segmentation process, the
proposed detector scan the image region by region. This detector
allows to estimate statistically the type of the region (object or
non-object). The method is tested under different underwater
conditions. Experimental results show that the proposed approach
outperforms conventional methods.
