基于融合多特征图切割的作物病害图像自动分割
Automatic segmentation of plant disease images based on graph cuts fusing multiple features
查看参考文献30篇
|
文摘
|
为提高黄瓜叶部病害图像的分割性能,该文提出一种基于融合多特征图切割的病害图像自动分割方法。首先采用一种新的阈值化方法对原始病害图像的红色分量进行二值化处理;然后融合纹理、灰度、距离3个特征构建能量函数的边界项,描述像素间的相似性;再利用分割区域像素与区域边界像素的红色分量差值自动建立能量函数的区域项,反映像素归属于背景和目标的程度;最后运用最大流算法求解能量函数得到分割结果。将该方法应用于黄瓜3种病害(靶斑病、霜霉病和白粉病)叶部图像分割中,并与OTSU算法及半自动图切割算法的分割结果进行比较。试验结果表明,该方法的平均错分率为1.81%,低于其他2种算法,平均分割速度约为2.34 s并无大幅增加。该研究可为黄瓜病害的自动识别和诊断提供技术参考。 |
|
其他语种文摘
|
Diseases in crops can lead to declines of production and quality, which cause economic losses in agricultural industry worldwide. Therefore, detection of the diseases in plants is extremely critical for sustainable agriculture. Many crop diseases perform on the leaves, and often present in the form of spots, so processing the leaf images is a feasible way for identifying and diagnosing diseases. Spots separation from the leaf is a very important step in the process of disease recognition and diagnosis. And the segmentation accuracy affects the reliability of the subsequent feature extraction and the accurateness of following classification directly. To improve the segmentation performance, an automatic segmentation algorithm based on graph cut which fused multiple features was put forward in this paper. Firstly the background was excluded by threshold method so as to speed up the image segmentation. The experimental comparison results showed that the segmentation effect for color images processed by OTSU algorithm, a simple adaptive threshold image segmentation method, was not very satisfactory. Therefore, a new method of threshold processing was studied here, which could remove most of the background and did not lose the disease spots. In addition, the red component of the original color images was used as the research objects of the new threshold method, since it had the strongest contrast compared with the green component and the blue component. Then three features, texture, gray level and distance were fused to build the boundary term of the energy function, which described the similarity between the pixels. Among them, for the sake of reducing the computational complexity and calculation time, the texture feature was simply defined by the one-dimensional entropy of images, which was the amount of information included by the gathered characteristics of gray level distribution. Moreover, in order to reflect the extent of the pixel belongs to the background or target, the red component difference between pixels in the image region and the region boundary was used to set up the area term of the energy function automatically. Finally the maximum flow algorithm was utilized to solve the established energy function, and the segmentation results were obtained. With the purpose of verifying the validity of the proposed algorithm, the method was applied to divide three kinds of cucumber disease (target spot, downy mildew and powdery mildew) leaf images. Each disease of 50 pictures, a total of 150 pictures were selected randomly as the experimental samples. And the OTSU algorithm and semi-automatic graph cuts algorithm were chosen as the contrast means. The experimental results demonstrate that the disease spots of the leaf images can be separated effectively when using the method proposed in this paper. The average error rate is 1.81%, which is lower than the other two algorithms, and its average segmentation time do not significantly increase. This study can provide a technical reference for the automatic identification and diagnosis of cucumber diseases in the future. |
|
来源
|
农业工程学报
,2014,30(17):212-219 【核心库】
|
|
DOI
|
10.3969/j.issn.1002-6819.2014.17.027
|
|
关键词
|
作物
;
病害
;
图像处理
;
图切割
;
多特征
;
黄瓜
|
|
地址
|
1.
中国科学技术大学信息科学技术学院, 合肥, 230026
2.
中国科学院合肥智能机械研究所, 合肥, 230031
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1002-6819 |
|
学科
|
农业工程;自动化技术、计算机技术 |
|
基金
|
江苏省人才项目
;
国家863计划
|
|
文献收藏号
|
CSCD:5252718
|
参考文献 共
30
共2页
|
|
1.
刁智华. 基于植物病斑的图像分割研究综述.
农机化研究,2012(10):1-5,11
|
CSCD被引
3
次
|
|
|
|
|
2.
尹建军. 不同生长状态下多目标番茄图像的自动分割方法.
农业工程学报,2006,22(10):149-153
|
CSCD被引
23
次
|
|
|
|
|
3.
刁智华. 复杂背景下棉花病叶害螨图像分割方法.
农业工程学报,2003,29(5):147-152
|
CSCD被引
1
次
|
|
|
|
|
4.
Camargo A. An image-processing based algorithm to automatically identify plant disease visual symptoms.
Biosystem Engineering,2009,102(1):9-21
|
CSCD被引
35
次
|
|
|
|
|
5.
王爱民. 基于矢量Prewitt算子的多尺度彩色图像边缘检测方法.
中国图象图形学报,1999,4(12):912-915
|
CSCD被引
1
次
|
|
|
|
|
6.
Shih F Y. Automatic seeded region growing for color image segmentation.
Image and Vision Computing,2005,23(10):877-886
|
CSCD被引
41
次
|
|
|
|
|
7.
汪建. 结合颜色和区域生长的茶叶图像分割算法研究.
茶叶科学,2011,31(1):71-77
|
CSCD被引
1
次
|
|
|
|
|
8.
毛罕平. 基于模糊C均值聚类的作物病害叶片图像分割方法研究.
农业工程学报,2008,24(9):136-140
|
CSCD被引
44
次
|
|
|
|
|
9.
关海鸥. 基于遗传模糊神经网络的植物病斑区域图像分割模型.
农业机械学报,2010,41(11):169-173
|
CSCD被引
1
次
|
|
|
|
|
10.
Meyer G E. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images.
Computers and Electronics in Agriculture,2004,42(3):161-180
|
CSCD被引
23
次
|
|
|
|
|
11.
Huang K Y. Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features.
Computers and Electronics in Agriculture,2007,57(1):3-11
|
CSCD被引
10
次
|
|
|
|
|
12.
郭宝龙. 基于图切割的图像自动分割方法.
模式识别与人工智能,2011,24(5):604-609
|
CSCD被引
4
次
|
|
|
|
|
13.
刘丽. 图像纹理特征提取方法综述.
中国图象图形学报,2009,14(4):622-635
|
CSCD被引
197
次
|
|
|
|
|
14.
冯建辉. 基于灰度共生矩阵提取纹理特征图像的研究.
北京测绘,2007(3):19-22
|
CSCD被引
50
次
|
|
|
|
|
15.
首照宇. 结合纹理特征改进的GBIS图像分割方法.
计算机应用研究,2013,30(7):2216-2222
|
CSCD被引
1
次
|
|
|
|
|
16.
杨国庆. 基于纹理特征的自然图像鉴别方法.
计算机应用研究,2010,27(7):2783-2785
|
CSCD被引
3
次
|
|
|
|
|
17.
Idrissa M. Texture classification using Gabor filters.
Pattern Recognition Letters,2002,23(9):1095-1102
|
CSCD被引
15
次
|
|
|
|
|
18.
范亚会. 采用UTPLBP的纹理图像分割.
光电工程,2013,40(5):113-119
|
CSCD被引
2
次
|
|
|
|
|
19.
刘建龙.
基于图论的图像分割算法研究,2006
|
CSCD被引
5
次
|
|
|
|
|
20.
Boykov Y. Graph cuts and efficient ND image segmentation.
International Journal of Computer Vision,2006,70(2):109-131
|
CSCD被引
101
次
|
|
|
|
|
了解气象卫星更多信息，请访问