Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (3): 129-134.
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HUANG Fen1, YU Qi1, YAO Xia2, SHANG Guiyan2, ZHU Yan2, WU Yanlian1, HUANG Yu2
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黄 芬1,于 琪1,姚 霞2,商贵艳2,朱 艳2,伍艳莲1,黄 宇2
Abstract: Wheat canopy image under the natural light has the feature of nonuniform illumination, complicated background with shadows. A K-means clustering algorithm based on HSI color space is proposed to conquer the problem of low accuracy and over segmentation existing in classic image segmentation algorithm. To restrain B weight, [R+G-B] is used to normalize color images in RGB space. After transforming the normalized image from RGB to HSI color space, the different methods of K-means cluster used to the H weight depend on whether the sunlight is uniform or not. The final image is gained after using mathematical morphology and noise-removal process. The experiments show that compared with K-means cluster processing in Lab space and Otsu algorithm based on H weight, the method based on H weight can avoid over segmentation and has accurate segmentation results in different N fertilization, different illumination and different periods.
Key words: wheat canopy image segmentation, HSI color space, H weight, K-means clustering
摘要: 大田环境下小麦冠层图像具有光照不均匀、背景复杂及阴影遮挡等特点,经典图像分割算法存在精度低、过分割等问题,提出一种基于HSI空间下H分量的K均值聚类算法。使用[R+G-B]归一化处理RGB空间下的彩色图像,以抑制其B分量;将归一化图像进行RGB到HSI的颜色空间转化;根据光照是否均匀,使用K均值聚类算法对彩色图像的H分量进行不同的聚类处理,经形态学开运算及去噪处理获得最终目标图像。实验表明,该方法对不同施氮量、不同光照、不同生长时期小麦冠层图像的分割效果较好,相对基于Lab空间的K-means聚类分割,该方法可一定程度避免过分割现象;相对基于H分量的Otsu算法,对光照不均匀图像分割更完整,对复杂背景图像分割更精确。
关键词: 小麦冠层图像分割, HSI颜色空间, H分量, K均值聚类
HUANG Fen1, YU Qi1, YAO Xia2, SHANG Guiyan2, ZHU Yan2, WU Yanlian1, HUANG Yu2. K-means clustering segmentation for H weight of wheat canopy image[J]. Computer Engineering and Applications, 2014, 50(3): 129-134.
黄 芬1,于 琪1,姚 霞2,商贵艳2,朱 艳2,伍艳莲1,黄 宇2. 小麦冠层图像H分量的K均值聚类分割[J]. 计算机工程与应用, 2014, 50(3): 129-134.
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http://cea.ceaj.org/EN/Y2014/V50/I3/129