本文關(guān)鍵詞： 信息處理技術(shù) 聲納圖像分割 模糊C均值聚類 Markov隨機(jī)場(chǎng) 小波域 迭代條件模型算法　出處：《兵工學(xué)報(bào)》2017年05期 　論文類型：期刊論文

【摘要】：聲納圖像成像質(zhì)量差、特征信息弱,目標(biāo)分割存在一定困難,為此提出一種融合多尺度統(tǒng)計(jì)信息的模糊C均值(FCM)聚類與Markov隨機(jī)場(chǎng)(MRF)的小波域聲納圖像分割算法。小波域中低頻信息統(tǒng)計(jì)特性描述了低頻不同區(qū)域像素聚類情況,高頻信息反映了該方向紋理特征,依據(jù)低頻子帶的統(tǒng)計(jì)峰值選取FCM初始聚類中心,應(yīng)用小波域FCM聚類算法對(duì)聲納圖像進(jìn)行預(yù)分割,抑制噪聲的影響,提高了預(yù)分割的準(zhǔn)確性;構(gòu)建初分割后圖像的多尺度MRF模型,尺度間節(jié)點(diǎn)標(biāo)記的相關(guān)性采用1階Markov性表征,尺度內(nèi)構(gòu)建2階鄰域系統(tǒng)描述系數(shù)間的標(biāo)記聯(lián)系,標(biāo)記場(chǎng)采用雙點(diǎn)多級(jí)邏輯模型建模,同一標(biāo)記的系數(shù)特征場(chǎng)采用高斯模型建模,彌補(bǔ)了MRF算法中層次信息和輪廓信息描述的不足;應(yīng)用迭代條件模型算法求其最小能量下的標(biāo)記場(chǎng),實(shí)現(xiàn)聲納圖像分割。從視覺(jué)主觀效果和客觀評(píng)價(jià)指標(biāo)兩方面的實(shí)驗(yàn)結(jié)果驗(yàn)證表明,該算法分割聲納圖像均優(yōu)于FCM聚類算法和MRF算法,分割的聲納圖像邊緣與細(xì)節(jié)的清晰度、精細(xì)度均有一定程度改善。
[Abstract]:Sonar image imaging quality is poor, feature information is weak, target segmentation is difficult. In this paper, a fuzzy C-means (FCM) clustering and Markov random field (MRF) clustering for multiscale statistical information are proposed. Wavelet domain sonar image segmentation algorithm. The statistical characteristics of low frequency information in wavelet domain describe the low frequency pixel clustering in different regions. The high frequency information reflects the texture feature of this direction. According to the statistical peak value of the low frequency sub-band, the initial clustering center of FCM is selected, and the wavelet domain FCM clustering algorithm is used to pre-segment the sonar image to suppress the influence of noise. The accuracy of presegmentation is improved. The multi-scale MRF model of the first segmentation image is constructed. The correlation of the node markers between scales is characterized by the first-order Markov, and the marker relation between the describing coefficients of the second-order neighborhood system is constructed within the scale. The label field is modeled by two-point and multi-level logic model, and Gao Si model is used to model the coefficient characteristic field of the same marker, which makes up for the deficiency of the description of hierarchical information and contour information in MRF algorithm. The iterative conditional model algorithm is used to find the label field under the minimum energy, and the sonar image segmentation is realized. The experimental results from the visual subjective effect and the objective evaluation index show that the proposed method can be used to segment the sonar image. This algorithm is better than FCM clustering algorithm and MRF algorithm, and the edge and detail of the sonar image segmentation are improved to some extent.
【作者單位】： 三峽大學(xué)水電工程智能視覺(jué)監(jiān)測(cè)湖北省重點(diǎn)實(shí)驗(yàn)室;三峽大學(xué)計(jì)算機(jī)與信息學(xué)院;
【基金】：國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(U1401252);國(guó)家自然科學(xué)基金項(xiàng)目(61272237) 水電工程智能視覺(jué)監(jiān)測(cè)湖北省重點(diǎn)實(shí)驗(yàn)室開(kāi)放基金項(xiàng)目(2015KLA05)
【分類號(hào)】：TB566;TP391.41
，

本文編號(hào)：1493031