Detection of Objects in Underwater Images Based on the Discrete Fractional Brownian Random Field

Under the influence of the lighting condition and some character of water media, the underwater images have low contrast, unbalance gray scales, fuzzy edge of objects and large quantity of noise which will appear with the movement of vehicle. For the mentioned above factors, when traditional methods are used to dispose underwater images, the regions of objects can’t be located exactly, details of objects are lost, and shapes of objects are distorted. Considering the objects detected in underwater images are often artificial, this paper proposes a method of underwater image segmentation based on the discrete ractional Brownian Random Field by combining the character of underwater images with the fractal theory. Comparing with the results obtained by the algorithms based on Ostu and Maximum Entropy, it shows that the presented method is robust, and it is efficient in underwater images segmentation.