發(fā)布時(shí)間：2018-05-09 14:06

  本文選題：機(jī)器視覺 + 圖像處理�。� 參考：《光子學(xué)報(bào)》2017年07期

【摘要】：單一特征識(shí)別的鎢礦石初選準(zhǔn)確率低,穩(wěn)定性差,本文提出結(jié)合模糊支持向量機(jī)和D-S證據(jù)理論相的多特征鎢礦石識(shí)別方法.對(duì)礦石圖像預(yù)處理后,分別提取礦石的顏色、灰度和紋理等3類視覺特征,對(duì)這3類視覺特征進(jìn)行模糊分類得到各自的信任度,再以這3類信任度為獨(dú)立證據(jù),采用D-S證據(jù)理論對(duì)3類證據(jù)進(jìn)行融合,并依據(jù)分類判決規(guī)則得到最終的識(shí)別結(jié)果.試驗(yàn)結(jié)果表明,通過D-S理論對(duì)模糊向量機(jī)證據(jù)的融合,鎢礦石初選的正確識(shí)別率達(dá)到96%以上,其準(zhǔn)確率和穩(wěn)定性較單一特征均有大幅度提高,滿足生產(chǎn)過程中初選工藝的要求.
[Abstract]:The tungsten ore primary selection with single feature is low in accuracy and poor in stability. In this paper, a multi feature tungsten ore identification method combining fuzzy support vector machine and D-S evidence theory is proposed. After preprocessing the ore image, 3 kinds of visual features, such as color, gray scale and texture, are extracted respectively, and the 3 types of visual features are classified by fuzzy classification. The trust degree of the 3 types of trust is the independent evidence, and the 3 kinds of evidence are fused with the D-S evidence theory, and the final recognition results are obtained according to the classification rules. The experimental results show that the correct recognition rate of the tungsten ore primary selection is above 96% through the fusion of the fuzzy vector machine evidence by the D-S theory, and its accuracy and stability are compared. The single characteristics have been greatly improved to meet the requirements of the primary process in the production process.

【作者單位】： 南昌大學(xué)機(jī)電工程學(xué)院;江西理工大學(xué)機(jī)電工程學(xué)院;
【基金】：國家自然科學(xué)基金(No.71361014)資助~~
【分類號(hào)】：TD954;TP391.41

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本文編號(hào)：1866312