Growth Signatures of Rosette Plants from Time-Lapse Video
Growth Signatures of Rosette Plants from Time-Lapse Video
复制标题
DOI：
10.1109/tcbb.2015.2404810
复制
发表时间：
2015-11-01
影响因子：
4.5
通讯作者：
Torras, Carme
中科院分区：
文献类型：
--
作者：
Dellen, Babette;Scharr, Hanno;Torras, Carme
植物生长是一个动态的过程，植物早期发育过程的精确过程是植物研究的重要内容。在这项工作中，我们通过处理生长植物的延时视频来研究莲座状植物的生长，其中我们使用烟草(烟草)作为模式植物。在视频序列的每一帧中，使用树叶形状模型检测潜在的树叶。由于植物的复杂形状以及它们在图像中的外观变化，这些检测很容易出错，这取决于叶片的移动、叶片的生长和光照条件。为了解决这个问题，我们使用了一种新的基于图的跟踪算法，它可以通过连接一系列相邻帧上的叶检测来弥合序列中的差距。我们使用拟合叶模型的重叠作为成对相似性度量，并禁止在单个帧内链接叶检测的图边。我们在一组烟草-植物生长序列上测试了该方法，可以跟踪植物的第一片叶，包括部分或临时遮挡的第一片叶，沿着完整的序列，证明了该方法在植物生长自动分析中的适用性。所有苗木表现出大致相同的生长行为，并发现了一个特征的生长特征。
Plant growth is a dynamic process, and the precise course of events during early plant development is of major interest for plant research. In this work, we investigate the growth of rosette plants by processing time-lapse videos of growing plants, where we use Nicotiana tabacum (tobacco) as a model plant. In each frame of the video sequences, potential leaves are detected using a leaf-shape model. These detections are prone to errors due to the complex shape of plants and their changing appearance in the image, depending on leaf movement, leaf growth, and illumination conditions. To cope with this problem, we employ a novel graph-based tracking algorithm which can bridge gaps in the sequence by linking leaf detections across a range of neighboring frames. We use the overlap of fitted leaf models as a pairwise similarity measure, and forbid graph edges that would link leaf detections within a single frame. We tested the method on a set of tobacco-plant growth sequences, and could track the first leaves of the plant, including partially or temporarily occluded ones, along complete sequences, demonstrating the applicability of the method to automatic plant growth analysis. All seedlings displayed approximately the same growth behavior, and a characteristic growth signature was found.