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基于紋理和顏色特征的甜瓜缺陷識(shí)別
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西北民族大學(xué)中青年科研基金資助項(xiàng)目（X2007-008）


Defect Detection of Muskmelon Based on Texture Features and Color Features
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    摘要:

    為了提高硬皮甜瓜缺陷分類(lèi)的正確率，提取基于紋理和顏色的綜合特征，采用支持向量機(jī)分類(lèi)器構(gòu)造了甜瓜缺陷的自動(dòng)檢測(cè)系統(tǒng)。對(duì)甜瓜圖像可疑區(qū)進(jìn)行了紋理分析，提取灰度共生矩陣的4個(gè)特征參數(shù)，經(jīng)過(guò)比較實(shí)驗(yàn)得出，對(duì)比度和角二階矩2個(gè)參數(shù)對(duì)甜瓜瓜蒂、花萼、擦傷和霉變有明顯的可區(qū)分性。在可疑區(qū)域上提取了由R、G、B分量及其算術(shù)運(yùn)算組成的12種顏色特征，通過(guò)實(shí)驗(yàn)篩選出4種具有較好區(qū)分性的顏色特征。實(shí)驗(yàn)結(jié)果表明，由這些優(yōu)選出的紋理與顏色特征組成的綜合特征及支持向量機(jī)分類(lèi)器對(duì)甜瓜缺陷的識(shí)別正確率達(dá)到92.2%。

    Abstract:

    In order to improve the accuracy of muskmelon’s defect detection, an automatic defect detection system based on support vector machine (SVM) was set up by adopting complex features of texture and color. Four textural parameters and twelve color features of combinations from RGB were tested for the discriminability in stem, calyx, bruise and mildew. Through the experiments, two textural and four color features with good discriminability were selected and treated as the complex features. The results indicated that with the complex features and SVM, the accuracy of classification on muskmelons was up to 92.2%. 

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王書(shū)志,張建華,馮全.基于紋理和顏色特征的甜瓜缺陷識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2011,42(3):175-179. Wang Shuzhi,Zhang Jianhua,Feng Quan. Defect Detection of Muskmelon Based on Texture Features and Color Features[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(3):175-179.

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