本文選題：圖像處理 + 紅棗紋理檢測��； 參考：《石河子大學(xué)》2017年碩士論文

【摘要】：本研究在綜合國內(nèi)外有關(guān)紋理檢測研究的基礎(chǔ)上,結(jié)合新疆地區(qū)紅棗產(chǎn)業(yè)的發(fā)展現(xiàn)狀,提出了基于OpenCV的紅棗紋理檢測研究的課題。哈密大棗作為新疆地理標(biāo)志產(chǎn)品之一,能夠在惡劣環(huán)境中生長,營養(yǎng)豐富。本文以哈密大棗為研究對象,以O(shè)penCV圖像處理函數(shù)庫為核心,結(jié)合數(shù)字圖像處理技術(shù),最終依據(jù)哈密大棗的表面紋理外部特征對哈密大棗進(jìn)行判定,實現(xiàn)分級。同時,本課題通過MATLAB軟件進(jìn)行算法研究,設(shè)計基于OpenCV的數(shù)字圖像處理系統(tǒng),界面友好,能夠滿足一些數(shù)字圖像處理需求,為以后的研究奠定基礎(chǔ)。基于Open CV的紅棗紋理檢測研究有利于提高哈密干棗外觀品質(zhì)等級分級效率,解放人力,縮小從采收包裝到大量投放市場的時間,可以保證品質(zhì)的優(yōu)良,對哈密大棗的市場競爭力有一定的提升作用。本論文主要研究成果如下:1、運(yùn)用二維離散小波變換,對哈密大棗圖像進(jìn)行去噪并增強(qiáng)。利用單尺度二維離散小波對哈密紅棗圖像進(jìn)行單尺度分解及重構(gòu)。單尺度二維離散小波分解可以得到低頻信號和高頻信號,低頻表示輪廓,高頻反應(yīng)細(xì)節(jié)和混入的噪聲,通過對低頻部分進(jìn)行增強(qiáng),高頻部分進(jìn)行削弱來增強(qiáng)低頻、抑制高頻,從而達(dá)到哈密紅棗圖像的去噪和增強(qiáng)目的。2、對灰度共生矩陣的最大概率、相關(guān)性、對比度、能量、同質(zhì)性和熵六個特征參數(shù)進(jìn)行[0,1]歸一化,并將結(jié)果作為BP神經(jīng)網(wǎng)絡(luò)和ANFIS的輸入向量,分別比較了BP神經(jīng)網(wǎng)絡(luò)梯度下降法、擬牛頓法和共軛梯度法三種訓(xùn)練方法。建立了ANFIS對哈密干棗表面紋理的評價模型,并對預(yù)測結(jié)果進(jìn)行閾值化。結(jié)果表明BP神經(jīng)網(wǎng)絡(luò)中單隱含層BP神經(jīng)網(wǎng)絡(luò)擬牛頓法的收斂速度最快,雙隱含層BP神經(jīng)網(wǎng)絡(luò)擬牛頓法精度最高,預(yù)測精度為89.66%。ANFIS算法預(yù)測結(jié)果閾值化后,預(yù)測精度為93.33%。3、對哈密大棗二值圖進(jìn)行形態(tài)學(xué)的腐蝕和膨脹進(jìn)行輪廓提取,以直徑為50像素的圓形結(jié)構(gòu)進(jìn)行形態(tài)學(xué)運(yùn)算時,得到的輪廓圖像滿足要求,并對輪廓圖進(jìn)行了質(zhì)心提取并標(biāo)記。通過二值圖與輪廓圖的差運(yùn)算,再經(jīng)過開運(yùn)算得到紅棗中心區(qū)域的連通域圖,消除邊緣影響,并標(biāo)記了各連通域質(zhì)心。4、為了評價哈密大棗表面紋理情況,提出了兩種連通域疏密度算法,以哈密大棗質(zhì)心為坐標(biāo)原點(diǎn)的連通域疏密度評價算法和以哈密大棗各連通域質(zhì)心的橫縱坐標(biāo)平均值為坐標(biāo)原點(diǎn)的連通域疏密度評價算法。通過計算各連通域質(zhì)心到坐標(biāo)原點(diǎn)的距離均值來表示連通域的疏密情況。運(yùn)用優(yōu)化C,g參數(shù)的lib SVM模型對兩種算法結(jié)果進(jìn)行分類,結(jié)果表明以哈密大棗各連通域質(zhì)心的橫縱坐標(biāo)平均值為坐標(biāo)原點(diǎn)的連通域疏密度評價算法效果較優(yōu),識別準(zhǔn)確率為93%。5、通過Qt和OpenCV開發(fā)了數(shù)字圖像處理系統(tǒng),實現(xiàn)紅棗圖像的一些處理功能,并且可進(jìn)行功能拓展和二次開發(fā)。在紅棗的動態(tài)追蹤中采用OpenCV函數(shù)庫,對灰度圖進(jìn)行行列求和運(yùn)算,得到像素和最小的行列位置,用10×10像素的黑色方形標(biāo)記鎖定紅棗位置,實現(xiàn)動態(tài)追蹤。
[Abstract]:Based on the comprehensive research on texture detection, combined with the current development of red dates industry in Xinjiang area, put forward the study of the OpenCV red dates texture detection based on topic. Hami jujube as Xinjiang geographical indication products, can grow in harsh environments rich in nutrients. Taking Hami jujube as the research object, based on OpenCV the image processing function library as the core, combined with digital image processing technology, based on the external characteristics of the surface texture of Hami jujube of Hami jujube were judged, achieve classification. At the same time, the algorithm through the MATLAB software design of the digital image processing system based on OpenCV interface, which can meet the needs of some digital image processing, to lay the foundation for the future research. Based on the detection of Open CV can improve the appearance quality of red dates texture grade of Hami jujube classification efficiency, solution Put human, reduced from harvesting packaging to a large number of time to market, can guarantee the excellent quality, the market competitiveness of Hami jujube have a role in upgrading. The main results are as follows: 1, using two-dimensional discrete wavelet transform, the Hami jujube image denoising and enhancement. The discrete wavelet decomposition and single scale the reconstruction of the Hami red dates image using single scale single scale. Two dimensional discrete wavelet decomposition can get low frequency signal and high frequency signal, low frequency high frequency response said contour details and mixed noise, were enhanced by the low frequency part and the high frequency part weakened to enhance the low frequency, high frequency suppression, so as to achieve image denoising and Hami red dates to strengthen the.2, the maximum probability, gray level co-occurrence matrix correlation, contrast, energy, homogeneity and entropy of six characteristic parameters of [0,1] normalization, and the results for the As the input vectors of BP neural network and ANFIS, respectively, compared with BP neural network gradient descent method, quasi Newton method and conjugate gradient method, three kinds of training methods. To establish the evaluation of Hami jujube surface texture of the ANFIS model, and the threshold of prediction results. The results show that the convergence rate of the single hidden layer BP neural network the quasi Newton method of fast BP neural network, BP neural network with double hidden layers quasi Newton method with the highest accuracy, the prediction accuracy of 89.66%.ANFIS prediction results after thresholding algorithm, the prediction accuracy is 93.33%.3, the Hami jujube two value image morphological erosion and dilation of contour extraction, with the diameter of 50 pixels of the circular structure the morphological operation, contour image to meet the requirements, and the outline of the centroid extraction and mark. By two value difference operation graph and contour map, and then after the opening operation to get red dates center area Connected graph, eliminate edge effects, and marked each connected domain centroid.4, in order to evaluate the surface texture of Hami jujube, put forward two kinds of density connected domain algorithm, taking Hami jujube centroid as the connected domain density evaluation algorithm and the coordinate origin in Hami jujube each connected region centroid of the horizontal and vertical coordinates of the average value for the connected domain the degree of evaluation coordinate algorithm. Said the density of connected domain by calculating the centroid coordinates of the connected domain. The mean distance to optimize the use of C, lib SVM g model parameters to classify the two kinds of algorithm results, the results show that the horizontal and vertical coordinates to Hami jujube each connected domain centroid average domain density the evaluation algorithm has better effect with the origin of coordinates, the accuracy rate is 93%.5, the Qt and OpenCV developed a digital image processing system, image processing functions to achieve some red dates, and. Row function expansion and two development. In the dynamic tracking of red dates, OpenCV function library is used to row and row the grayscale map to get the pixel and the smallest row position. The location of red dates is locked with 10 * 10 pixels black square mark, and dynamic tracking is realized.

【學(xué)位授予單位】：石河子大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S225.93;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 焦亮;胡國清;Jahangir Alam SM;;基于機(jī)器視覺的隨機(jī)紋理瓷磚的分選系統(tǒng)[J];計算機(jī)系統(tǒng)應(yīng)用;2016年03期

2 李臣陽;高向川;張衛(wèi)黨;;基于共軛梯度法的低復(fù)雜度信道估計[J];通信技術(shù);2015年11期

3 王紅玉;馮筠;崔磊;賀小偉;邱實;;應(yīng)用顯著紋理特征的醫(yī)學(xué)圖像配準(zhǔn)[J];光學(xué)精密工程;2015年09期

4 毋小省;文運(yùn)平;孫君頂;FAN Guo-liang;;基于紋理與特征選擇的前視紅外目標(biāo)識別[J];光電子·激光;2014年11期

5 鄭淑丹;鄭江華;石明輝;郭寶林;森巴提;孫志群;賈曉光;李曉瑾;;基于分形和灰度共生矩陣紋理特征的種植型藥用植物遙感分類[J];遙感學(xué)報;2014年04期

6 朱碧云;陳卉;;醫(yī)學(xué)圖像紋理分析的方法及應(yīng)用[J];中國醫(yī)學(xué)裝備;2013年08期

7 海濤;傅戈雁;;機(jī)器視覺技術(shù)在谷物外觀品質(zhì)檢測研究現(xiàn)狀的分析[J];農(nóng)業(yè)裝備技術(shù);2013年04期

8 馬國兵;肖培如;;基于小波的圖像去噪研究綜述[J];工業(yè)控制計算機(jī);2013年05期

9 趙云華;李東海;柳炳利;武飛;;小波理論及其在地球化學(xué)數(shù)據(jù)處理中的應(yīng)用綜述[J];四川理工學(xué)院學(xué)報(自然科學(xué)版);2013年02期

10 任敏;;多傳感器遙感圖像紋理特征選取的研究[J];電腦知識與技術(shù);2013年09期

相關(guān)重要報紙文章 前1條

1 孫建兵;;哈密大棗 一個增收的神話[N];哈密日報(漢);2010年

相關(guān)博士學(xué)位論文 前1條

1 王凱;基于圖像紋理特征提取算法的研究及應(yīng)用[D];西南交通大學(xué);2013年

相關(guān)碩士學(xué)位論文 前7條

1 張昭豹;新疆紅棗消費(fèi)市場調(diào)查研究[D];新疆農(nóng)業(yè)大學(xué);2014年

2 劉照朋;房式紅棗烘房的設(shè)計與研究[D];河南農(nóng)業(yè)大學(xué);2014年

3 陶雪英;新疆紅棗競爭力分析[D];新疆農(nóng)業(yè)大學(xué);2013年

4 李娜;基于數(shù)學(xué)形態(tài)學(xué)的藻類圖像去噪算法研究[D];中國海洋大學(xué);2013年

5 王麗麗;基于計算機(jī)視覺的哈密大棗無損檢測分級技術(shù)及分級裝置研究[D];石河子大學(xué);2013年

6 范紅梅;基于特征的文檔圖像檢索技術(shù)研究與應(yīng)用[D];山東師范大學(xué);2010年

7 蘇娜;紅棗發(fā)酵酒加工工藝研究[D];西北農(nóng)林科技大學(xué);2008年

，

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