顯著性檢測方法及其在黃瓜病害圖像分割中的應用研究
發(fā)布時間：2019-07-03 10:14
【摘要】：最近幾年,圖像顯著性檢測是計算機視覺領域研究的熱點。圖像顯著性檢測的目的是能夠?qū)D像中感興趣的目標區(qū)域自動地檢測出來。對目標區(qū)域的檢測精度與檢測效率將直接影響到后續(xù)目標識別的性能。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行了應用研究。論文的主要研究工作如下:(1)提出了一種基于先驗信息和雙權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,以獲得良好的目標邊緣;其次,引入高斯權重和歐氏距離權重,以獲取精細化的顯著圖;接著,引入中心先驗和非顯著關聯(lián)先驗,以去除背景中的干擾信息;最后,通過非線性作用函數(shù)Sigmoid對得到的顯著圖進行調(diào)整優(yōu)化。在Berkeley和MSRA1000數(shù)據(jù)庫上進行測試。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,檢測精度達到93%,而且具有較低的算法時間復雜度。(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重計算方法和顯著種子選取方法,以增強算法的魯棒性;其次,通過流形排序計算,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行平滑調(diào)整;對調(diào)整后的顯著圖進行閾值分割,將得到的二值圖像與原圖像進行掩碼運算,得到最終分割結果。在MSRA1000圖像顯著性檢測數(shù)據(jù)庫上進行測試,準確率-召回率曲線顯示在相同召回率下準確率高于其它算法,并且具有較高的F-measure值。最后,將MREESD同PIDWSD進行了實驗對比,從實驗結果中看出,MREESD算法具有更強的魯棒性。(3)作物病害圖像分割精度對病害自動識別效果具有關鍵作用。針對復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行處理,以擴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)學膨脹操作對得到的病斑進行處理,以獲取更加飽滿的病斑。實驗結果表明,本文所提的算法在提取出的病斑上更加精確,錯分率均低于5%。通過對黃瓜典型的四種病害進行分析,提取病害特征;最后,采用BP神經(jīng)網(wǎng)絡分類器對黃瓜病害進行分類識別,識別率達到83%以上,從而驗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.
【學位授予單位】：南京農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：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.
【學位授予單位】：南京農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：S436.421;TP391.41
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