Seabed Image Texture Segmentation and Classification Based on Nonsubsampled Contourlet Transform

In this paper, a new split and merge algorithm based on nonsubsampled contourlet transform for automatic segmentation and classification of sea- floor images is presented. This transform is a redundant version of contourlet transform which is a new two-dimensional extension of wavelet transform using multiscale and directional filter banks. It allows analysis of images at various scales as well as directions, which effectively capture smooth contours that are the dominant features in seabed images. The introduced redundancy brings simplicity and accuracy for feature calculation. The proposed method provides a fast toot with enough accuracy that can be implemented in a parallel structure for real-time processing. In addition, the simulation results are compared with the results of wavelet-based methods as well as other known techniques to show the effectiveness of the proposed algorithm.