An improved Fuzzy C-means algorithm based on gray-scale histogram for underwater image segmentation

Due to the assimilation of the water and uneven lightness, the underwater images would have low S/N and the detail is fuzzy. If traditional methods are used to dispose underwater images directly, it is unlikely to obtain satisfactory results. Though traditional Fuzzy C-means algorithm could sometimes divide the image into object and background, its time-consuming computation is often an obstacle. As the mission of the vision system of autonomous underwater vehicle (AUV), it should deal with the information about the object in the complex environment rapidly and exactly for AUV to use the obtained result for the next task. So, aiming at realizing a clustering quickly on the basis of providing a high qualified segmentation of an underwater image, a novel fuzzy C-means algorithm based on gray-scale histogram for underwater image segmentation is proposed. Experimental results indicate that the novel algorithm can get a better segmentation result and the processing time of each image is reduced and enhance efficiency and satisfy the request of highly real-time effectiveness of AUV.