Résumé:
Object detection is an important process in image processing, it aims to detect instances of semantic
objects of a certain class in digital images and videos. Object detection has applications in many areas
of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson–Gauss theory, because it can
accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we
denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into
objects. The method is tested under different underwater conditions. Experimental results show that
the proposed approach outperforms state of the art methods.
