顯著性檢測方法及其在黃瓜病害圖像分割中的應(yīng)用研究
發(fā)布時(shí)間：2019-07-03 10:14
【摘要】：最近幾年,圖像顯著性檢測是計(jì)算機(jī)視覺領(lǐng)域研究的熱點(diǎn)。圖像顯著性檢測的目的是能夠?qū)D像中感興趣的目標(biāo)區(qū)域自動地檢測出來。對目標(biāo)區(qū)域的檢測精度與檢測效率將直接影響到后續(xù)目標(biāo)識別的性能。本文圍繞如何提高顯著性檢測算法的精度和檢測效率展開相關(guān)的理論方法研究,并將提出的顯著性檢測算法在黃瓜病害圖像處理中進(jìn)行了應(yīng)用研究。論文的主要研究工作如下:(1)提出了一種基于先驗(yàn)信息和雙權(quán)重的顯著性檢測算法(Saliency detection algorithm based on prior information and double weights,P I DWSD)。PIDWSD 算法主要是為了解決上下文感知顯著性檢測算法(Context-Aware saliency detection,CA)中存在的邊緣丟失及檢測精度不高的問題。PIDWSD算法首先使用超像素將圖像分塊,以獲得良好的目標(biāo)邊緣;其次,引入高斯權(quán)重和歐氏距離權(quán)重,以獲取精細(xì)化的顯著圖;接著,引入中心先驗(yàn)和非顯著關(guān)聯(lián)先驗(yàn),以去除背景中的干擾信息;最后,通過非線性作用函數(shù)Sigmoid對得到的顯著圖進(jìn)行調(diào)整優(yōu)化。在Berkeley和MSRA1000數(shù)據(jù)庫上進(jìn)行測試。與其它顯著性檢測算法相比,該方法不僅能很好地解決邊緣丟失問題,檢測精度達(dá)到93%,而且具有較低的算法時(shí)間復(fù)雜度。(2)提出了一種融合流形排序和能量方程的顯著性檢測算法(Saliency detection algorithm combining manifold ranking and energy equation,MREESD)。該算法主要是為了解決傳統(tǒng)顯著性檢測算法檢測精度不高且顯著種子選取魯棒性不足的問題。首先,使用超像素方法將圖像分塊,提出了一種新的超像素間權(quán)重計(jì)算方法和顯著種子選取方法,以增強(qiáng)算法的魯棒性;其次,通過流形排序計(jì)算,以獲取較優(yōu)的顯著圖;為使得顯著圖更加精確,利用能量方程對得到的顯著圖進(jìn)行平滑調(diào)整;對調(diào)整后的顯著圖進(jìn)行閾值分割,將得到的二值圖像與原圖像進(jìn)行掩碼運(yùn)算,得到最終分割結(jié)果。在MSRA1000圖像顯著性檢測數(shù)據(jù)庫上進(jìn)行測試,準(zhǔn)確率-召回率曲線顯示在相同召回率下準(zhǔn)確率高于其它算法,并且具有較高的F-measure值。最后,將MREESD同PIDWSD進(jìn)行了實(shí)驗(yàn)對比,從實(shí)驗(yàn)結(jié)果中看出,MREESD算法具有更強(qiáng)的魯棒性。(3)作物病害圖像分割精度對病害自動識別效果具有關(guān)鍵作用。針對復(fù)雜背景下黃瓜葉部病害分割精度不高的問題,本文將顯著性檢測應(yīng)用于自然環(huán)境的黃瓜葉部病害的圖像處理中。首先,通過顯著性檢測算法提取出黃瓜病害葉片;其次,利用超綠特征對病害葉片進(jìn)行處理,以擴(kuò)大綠色正常部分和非綠色病斑部分的灰度差距,通過閾值分割出病斑;最后,利用形態(tài)學(xué)膨脹操作對得到的病斑進(jìn)行處理,以獲取更加飽滿的病斑。實(shí)驗(yàn)結(jié)果表明,本文所提的算法在提取出的病斑上更加精確,錯(cuò)分率均低于5%。通過對黃瓜典型的四種病害進(jìn)行分析,提取病害特征;最后,采用BP神經(jīng)網(wǎng)絡(luò)分類器對黃瓜病害進(jìn)行分類識別,識別率達(dá)到83%以上,從而驗(yàn)證了本文所提的顯著性檢測算法在病害圖像處理中的可行性和實(shí)用性。
[Abstract]:In recent years, image saliency detection is a hot topic in the field of computer vision. The purpose of the image saliency detection is to be able to automatically detect the target area of interest in the image. The detection accuracy and the detection efficiency of the target area will directly affect the performance of the subsequent target recognition. This paper studies on how to improve the accuracy of the significance detection algorithm and the detection efficiency, and applies the proposed significance detection algorithm to the image processing of cucumber diseases. The main research work of the thesis is as follows: (1) a significance detection algorithm based on a priori information and a double-weight is proposed (Salience detection algorithm based on priority information and double weight, P I DWSD). The PIDWSD algorithm is mainly to solve the problem of low edge loss and low detection accuracy in the context-aware significance detection algorithm (CA). The PIDWSD algorithm first uses the super-pixel to block the image to obtain a good target edge; secondly, introducing the Gaussian weight and the Euclidean distance weight to obtain a refined saliency map; then, introducing a center prior and non-significant correlation a priori to remove the interference information in the background; and finally, And the obtained saliency map is adjusted and optimized by the non-linear action function Sigmoid. Testing was performed on the Berkeley and MRA1000 databases. Compared with other significance detection algorithms, the method not only can well solve the problem of edge loss, the detection accuracy reaches 93%, but also has lower algorithm time complexity. (2) A significance detection algorithm for the ordering and energy equation of a fusion manifold (MREESD) is proposed. The algorithm is mainly used to solve the problem that the traditional significance detection algorithm is not high in detection precision and is not sufficiently robust to select a significant seed. Firstly, the super-pixel method is used to block the image, a new method for calculating the weight between the super-pixels and a method for selecting a significant seed is proposed, so that the robustness of the algorithm is enhanced; secondly, the optimal saliency map is obtained by the manifold sorting calculation, so that the saliency map is more accurate, And performing a threshold segmentation on the adjusted saliency map, and performing mask operation on the obtained binary image and the original image to obtain a final segmentation result. On the MRA1000 image significance test database, the accuracy-recall rate curve shows that the accuracy rate is higher than other algorithms at the same recall rate, and has a higher F-mean value. Finally, the MREESD is compared with the PIDWSD, and it can be seen from the experimental results that the MREESD algorithm is more robust. (3) The image segmentation accuracy of crop disease plays a key role in the automatic recognition of disease. Aiming at the problem of low segmentation precision of the cucumber leaf part under the complex background, the method is used in the image processing of the disease of the cucumber leaf part of the natural environment. firstly, a cucumber disease blade is extracted by a saliency detection algorithm; secondly, the disease blade is treated by using the super-green characteristic to expand the gray difference of the green normal part and the non-green disease spot part, and the disease spot is divided by a threshold value; and finally, The acquired disease spot is treated by the morphological expansion operation so as to obtain a more plump disease spot. The experimental results show that the proposed algorithm is more accurate in the extracted lesions, and the error rate is less than 5%. By analyzing the four diseases typical of the cucumber, the disease characteristics are extracted; and finally, the BP neural network classifier is adopted to classify and identify the cucumber diseases, and the recognition rate is more than 83 percent, So that the feasibility and the practicability of the significance detection algorithm in the disease image processing are verified.
【學(xué)位授予單位】：南京農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：S436.421;TP391.41
本文編號：2509303
[Abstract]:In recent years, image saliency detection is a hot topic in the field of computer vision. The purpose of the image saliency detection is to be able to automatically detect the target area of interest in the image. The detection accuracy and the detection efficiency of the target area will directly affect the performance of the subsequent target recognition. This paper studies on how to improve the accuracy of the significance detection algorithm and the detection efficiency, and applies the proposed significance detection algorithm to the image processing of cucumber diseases. The main research work of the thesis is as follows: (1) a significance detection algorithm based on a priori information and a double-weight is proposed (Salience detection algorithm based on priority information and double weight, P I DWSD). The PIDWSD algorithm is mainly to solve the problem of low edge loss and low detection accuracy in the context-aware significance detection algorithm (CA). The PIDWSD algorithm first uses the super-pixel to block the image to obtain a good target edge; secondly, introducing the Gaussian weight and the Euclidean distance weight to obtain a refined saliency map; then, introducing a center prior and non-significant correlation a priori to remove the interference information in the background; and finally, And the obtained saliency map is adjusted and optimized by the non-linear action function Sigmoid. Testing was performed on the Berkeley and MRA1000 databases. Compared with other significance detection algorithms, the method not only can well solve the problem of edge loss, the detection accuracy reaches 93%, but also has lower algorithm time complexity. (2) A significance detection algorithm for the ordering and energy equation of a fusion manifold (MREESD) is proposed. The algorithm is mainly used to solve the problem that the traditional significance detection algorithm is not high in detection precision and is not sufficiently robust to select a significant seed. Firstly, the super-pixel method is used to block the image, a new method for calculating the weight between the super-pixels and a method for selecting a significant seed is proposed, so that the robustness of the algorithm is enhanced; secondly, the optimal saliency map is obtained by the manifold sorting calculation, so that the saliency map is more accurate, And performing a threshold segmentation on the adjusted saliency map, and performing mask operation on the obtained binary image and the original image to obtain a final segmentation result. On the MRA1000 image significance test database, the accuracy-recall rate curve shows that the accuracy rate is higher than other algorithms at the same recall rate, and has a higher F-mean value. Finally, the MREESD is compared with the PIDWSD, and it can be seen from the experimental results that the MREESD algorithm is more robust. (3) The image segmentation accuracy of crop disease plays a key role in the automatic recognition of disease. Aiming at the problem of low segmentation precision of the cucumber leaf part under the complex background, the method is used in the image processing of the disease of the cucumber leaf part of the natural environment. firstly, a cucumber disease blade is extracted by a saliency detection algorithm; secondly, the disease blade is treated by using the super-green characteristic to expand the gray difference of the green normal part and the non-green disease spot part, and the disease spot is divided by a threshold value; and finally, The acquired disease spot is treated by the morphological expansion operation so as to obtain a more plump disease spot. The experimental results show that the proposed algorithm is more accurate in the extracted lesions, and the error rate is less than 5%. By analyzing the four diseases typical of the cucumber, the disease characteristics are extracted; and finally, the BP neural network classifier is adopted to classify and identify the cucumber diseases, and the recognition rate is more than 83 percent, So that the feasibility and the practicability of the significance detection algorithm in the disease image processing are verified.
【學(xué)位授予單位】：南京農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：S436.421;TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 金春范;;大棚黃瓜種植技術(shù)及病蟲害防治[J];農(nóng)技服務(wù);2015年10期
2 高本國;;基于BP神經(jīng)網(wǎng)絡(luò)的水下管道圖像識別與判斷技術(shù)研究[J];電子技術(shù)與軟件工程;2015年20期
3 趙瑤池;胡祝華;;基于對數(shù)相似度約束Otsu的自然場景病害果實(shí)圖像分割[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2015年11期
4 孫曉鵬;李思慧;王璐;韓楓;魏小鵬;;耳廓點(diǎn)云形狀特征匹配的路徑跟隨算法[J];軟件學(xué)報(bào);2015年05期
5 王守覺;蔣寓文;譚樂怡;;利用多級量化局部紋理特征的圖像跟蹤算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2015年02期
6 田杰;韓冬;胡秋霞;馬孝義;;基于PCA和高斯混合模型的小麥病害彩色圖像分割[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2014年07期
7 靳廣清;左連翠;;圖的擬拉普拉斯矩陣前k個(gè)最大特征值和的上界[J];山東大學(xué)學(xué)報(bào)(理學(xué)版);2013年08期
8 王海青;姬長英;顧寶興;田光兆;;基于參數(shù)自適應(yīng)脈沖耦合神經(jīng)網(wǎng)絡(luò)的黃瓜目標(biāo)分割[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2013年03期
9 劉毅;黃兵;孫懷江;夏德深;;利用視覺顯著性與圖割的圖像分割算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2013年03期
10 楊守建;陳懇;;BP神經(jīng)網(wǎng)絡(luò)性能與隱藏層結(jié)構(gòu)的相關(guān)性探究[J];寧波大學(xué)學(xué)報(bào)(理工版);2013年01期
，本文編號：2509303
本文鏈接：http://sikaile.net/kejilunwen/ruanjiangongchenglunwen/2509303.html
最近更新
教材專著
