本文選題：聲納圖像分割 + 樹結構化的馬爾可夫隨機場(TS-MRF)　； 參考：《華中科技大學學報(自然科學版)》2017年05期

【摘要】：為解決聲納圖像本身特征信息較弱,而樹結構化的馬爾可夫隨機場(TS-MRF)算法在分割中過分依賴祖先節(jié)點,且在標號分割中僅考慮區(qū)域內部一致性而忽視區(qū)域邊緣的各向異性的問題,提出了一種模糊樹結構化的馬爾可夫隨機場(TS-MRF)模型的聲納圖像分割算法.在TS-MRF勢函數中引入廣義模糊算子,以模糊隸屬度作為像素相似度度量,將鄰域信息融入到分裂節(jié)點參數的確定中,使得先驗概率的刻畫更加精細.已知圖像觀測特征前提下定義分裂增益系數來反映分裂前、后標號后驗概率的比值,并將對增益系數的判斷作為確定二叉樹節(jié)點分裂的依據,降低求解后驗概率最大的計算復雜度.結合區(qū)域分裂合并方法完成對聲納圖像無監(jiān)督分割.實驗結果從視覺效果和客觀評價表明:本分割方法相比于傳統(tǒng)MRF和TS-MRF等分割算法,具有較高的分割精度和高魯棒性.
[Abstract]:In order to solve the problem that the feature information of sonar image is weak, the tree structured Markov random field (TS-MRF) algorithm relies too much on the ancestor nodes in segmentation. The anisotropy of the edge of the region is only considered in label segmentation. A fuzzy tree structured Markov random field (TS-MRF) model is proposed for sonar image segmentation. The generalized fuzzy operator is introduced into the TS-MRF potential function and the fuzzy membership degree is used as the pixel similarity measure. The neighborhood information is incorporated into the parameter determination of the split node, which makes the characterization of the priori probability more precise. Based on the known image observation characteristics, the splitting gain coefficient is defined to reflect the ratio of pre-splitting and post-label posteriori probability, and the judgment of gain coefficient is taken as the basis for determining the division of binary tree nodes. Reduce the computational complexity of the maximum posteriori probability. The unsupervised segmentation of sonar image is accomplished by combining region splitting and merging method. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the traditional MRF and TS-MRF segmentation algorithms.
【作者單位】： 三峽大學水電工程智能視覺監(jiān)測湖北省重點實驗室;三峽大學計算機與信息學院;
【基金】：國家自然科學基金(聯(lián)合基金)重點資助項目(U1401252);國家自然科學基金資助項目(61272237) 湖北省重點實驗室開放基金資助項目(2015KLA05)
【分類號】：TP391.41
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本文編號：1979898