Hough transform for robust segmentation of underwater multispectral images

A segmentation algorithm for underwater multispectral images based on the Hough transform (HT) is presented. The segmentation algorithm consists of three stages: The first stage consists in computing the HT of the original image and segmenting the desired object in its boundary. The HT has several known challenges such as the end point (infinite lines) and the connectivity problem, which lead to false contours. Most of these problems are canceled over the next two stages. The second stage starts by clustering the original image. Fuzzy C-means clustering segmentation technique is used to capture the local properties of the desired object. In the third stage, the edges of the clustering segmentation are extended to the closest HT detected lines. The boundary information (HT) and local properties (Fuzzy C-means) of the desired object are fused together and false contours are eliminated. The performance of the segmentation algorithm is demonstrated in underwater multispectral images generated in laboratory containing known objects of varying size and shape.