Environmentally adaptive segmentation algorithm for outdoor image segmentation
Environmentally adaptive segmentation algorithm for outdoor image segmentation
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DOI：
10.1016/s0168-1699(98)00037-4
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发表时间：
1998-12-01
影响因子：
8.3
通讯作者：
Slaughter, DC
中科院分区：
文献类型：
--
作者：
Tian, LF;Slaughter, DC
提出了一种环境自适应分割算法（EASA），用于野外植物检测.基于部分监督学习过程，该算法可以从户外农业领域的环境条件中学习，并实时构建图像分割查找表。实验结果表明，该算法能够适应室外大部分白天条件，如光源温度变化、土壤类型变化等。与在阳光充足的条件下训练的静态分割技术相比，EASA在部分多云和阴天条件下分别正确分类了26.9%和54.3%的对象像素，从而改善了图像分割。改进的图像分割的EASA技术也允许多达32倍的植物子叶被识别（叶形态）在阴天照明条件下相比，静态分割技术在阳光明媚的条件下训练。(C)1998 Elsevier Science B.V.保留所有权利。
An environmentally adaptive segmentation algorithm (EASA) was developed for outdoor field plant detection. Based on a partially supervised learning process, the algorithm can learn from environmental conditions in outdoor agricultural fields and build an image segmentation look-up table on-the-fly. Experiments showed that the algorithm can adapt to most daytime conditions in outdoor fields, such as changes in light source temperature and soil type. When compared to a static segmentation technique which was trained under sunny conditions, the EASA improved the image segmentation by correctly classifying 26.9 and 54.3% more object pixels under partially cloudy and overcast conditions, respectively. The improved image segmentation of the EASA technique also allowed up to 32 times more plant cotyledons to be recognized (by leaf morphology) under overcast lighting conditions when compared with a static segmentation technique trained under sunny conditions. (C) 1998 Elsevier Science B.V. All rights reserved.