.png){kind=icon}
返回
Environmentally adaptive segmentation algorithm for outdoor image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/S0168-1699(98)00037-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
computer vision
field plant
outdoor lighting
segmentation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.0W
被引数:
4.8W
机构
暂无机构信息
.png){kind=icon}
没有更多内容