张艳诚,毛罕平,胡波,李明喜.作物病害图像中重叠病斑分离算法[J].农业机械学报,2008,39(2):112-115.
.[J].Transactions of the Chinese Society for Agricultural Machinery,2008,39(2):112-115.
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作物病害图像中重叠病斑分离算法   [下载全文]
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DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  图像分割  数学形态学  极限腐蚀  测地重建  分水岭算法
基金项目:
张艳诚  毛罕平  胡波  李明喜
江苏大学
中文摘要:针对传统的分水岭分割算法的不足,应用了一种基于标记测地重建的分水岭算法对棉花重叠病斑图像进行分离。首先对病斑二值图像利用多尺度距离变换获得病斑的距离图像,通过极限腐蚀操作检测出标记种子;然后以种子标记为基础,运用形态学测地重建运算获取测地影响区骨架SKIZ——分水岭线;最后利用分水岭线与原病斑图像进行交集运算,从而得到分离的图像。运用该算法对棉花重叠病斑图像进行分离,试验结果表明:该方法能较好地将图像中的重叠病斑分离,并较好地保存病斑边缘信息,对图像中的微小结构和噪声不敏感,具有良好的鲁棒性。
Key Words:
Abstract: For accurately isolating the overlapping spots in crop disease images, an improved watershed algorithm named marker-reconstruction watershed algorithm was presented. Firstly, the binary spots image was transformed to distance image by multi-scale distance transform operator and makers (seed) were detected by using ultimate erosion. Then, geodesic SKIZ (i.e., watershed division lines) has been obtained by geodesic reconstruction operator constrained by marking seed image. Finally, the watershed division lines were subtracted from the original binary image. Applying the proposed method in cotton disease images, the experimental results indicated that the method could validly and effectively separate overlapping spots in the image, moreover, preferably protect the spots edge. This method is insensitive to noise and tiny outlier of the image.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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