This paper proposes a novel framework for fluorescence plant video processing. The plant research community is interested in the leaf-level photosynthetic analysis within a plant. A prerequisite for such analysis is to segment all leaves, estimate their structures, and track them over time. We identify this as a joint multi-leaf segmentation, alignment, and tracking problem. First, leaf segmentation and alignment are applied on the last frame of a plant video to find a number of well-aligned leaf candidates. Second, leaf tracking is applied on the remaining frames with leaf candidate transformation from the previous frame. We form two optimization problems with shared terms in their objective functions for leaf alignment and tracking respectively. A quantitative evaluation framework is formulated to evaluate the performance of our algorithm with four metrics. Two models are learned to predict the alignment accuracy and detect tracking failure respectively in order to provide guidance for subsequent plant biology analysis. The limitation of our algorithm is also studied. Experimental results show the effectiveness, efficiency, and robustness of the proposed method.

中文翻译：

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联合多叶分割，对齐和跟踪荧光植物视频

本文提出了一种新型的荧光植物视频处理框架。植物研究界对植物内叶片水平的光合作用分析感兴趣。进行此类分析的先决条件是将所有叶子分段，估计它们的结构并随时间跟踪它们。我们将其识别为联合多叶 细分，对齐， 和 追踪问题。首先，将叶子分割和对齐应用于植物视频的最后一帧，以找到许多对齐良好的叶子候选对象。第二，对剩余的帧应用叶子跟踪，并从前一帧进行叶子候选者转换。我们分别在叶片对齐和跟踪的目标函数中形成两个共享术语的优化问题。制定了量化评估框架，以四个指标评估我们算法的性能。学习了两个模型来分别预测对中精度和检测跟踪失败，以便为后续植物生物学分析提供指导。还研究了我们算法的局限性。实验结果表明了该方法的有效性，有效性和鲁棒性。