According to the characteristics of sonar image data with manifold feature,the sonar image detection method based on two-phase manifold partner clustering algorithm is proposed. Firstly,K-means block clustering based on euclidean distance is proposed to reduce the data set. Mean value,standard deviation,and gray minimum value are considered as three features based on the relatinship between clustering model and data structure. Then K-means clustering algorithm based on manifold distance is utilized clustering again on the reduced data set to improve the detection efficiency. In K-means clustering algorithm based on manifold distance,line segment length on the manifold is analyzed,and a new power function line segment length is proposed to decrease the computational complexity. In order to quickly calculate the manifold distance,new allsource shortest path as the pretreatment of efficient algorithm is proposed. Based on this,the spatial feature of the image block is added in the three features to get the final precise partner clustering algorithm. The comparison with the other typical clustering algorithms demonstrates that the proposed algorithm gets good detection result. And it has better adaptability by experiments of the different real sonar images.
According to the characteristics of sonar image data with big data feature, In order to accurately detect underwater objects of sonar image, a novel adaptive threshold FCM (Fuzzy Clustering Algorithm, FCM) based on frequency domain and time domain is proposed. Based on the relationship between sonar image data and big data, Firstly, wavelet de-noising method is used to smooth noise. After de-noising, the sonar image is blocked and each sub-block region is processed by two-dimensional discrete Fourier transform, their maximum amplitude spectrum used as frequency domain character, then time domain of mean and standard deviation, frequency domain of maximum amplitude spectrum are taken for character to complete block k-means clustering, the initial clustering center is determined, after that made use of FCM on sonar image detection, based on clustered image, adaptive threshold is constructed by the distribution of sonar image sea-bottom reverberation region, and final detection results of sonar image are completed. The comparison different experiments demonstrate that the proposed algorithm get good detection precision and adaptability.
An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.
WANG XingmeiYIN GuishengLIU GuangyuLIU ZhipengWANG Xiaowei
