基于聲納圖像處理的海底地貌分類研究.pdf

1、In this paper, the basic theory about MRF is introduced and Gauss-Rayleigh Mixture Model is presented according to the feature of sonar images. Comparing with the segmentation with traditional threshold method, the MRF m

2、ethod based on the Gauss-Rayleigh Mixture Model is suitable for the sonar image segmentation perfectly. Secondly, a scheme is proposed to extract the texture feature of sonar images using Gabor filters with optimal param

3、eters. The Gabor filters are designed with constrained parameters to reduce the complexity and to improve the calculation efficiency. Meanwhile, at each orientation, the optimal Gabor filter parameters will be selected w

4、ith the help of bandwidth parameters based on the Fisher criterion. This method can overcome some disadvantages of the traditional approaches of extracting texture features, and improve the recognition rate effectively.

5、Finally, taking the scale or resolution factor into consideration, two more efficient classification algorithms are proposed. One is an information fusion method, which combines both Gabor filters and fuzzy fractal dimen

6、sion, to extract features and to classify seabed. It can improve recognition rate effective. However, it wastes of time and memory. So another efficient classification algorithm, which is called Gabor wavelet, is applied

7、 to extract the features of sonar images. Concluding from the experiments, Gabor wavelet not only contains multi-resolution information of images but also indicates the multi-orientation feature of the images, which is u