Joint Multi-Leaf Segmentation, Alignment and Tracking from Fluorescence Plant Videos
This addresses a domain-specific need for plant researchers by providing a framework for analyzing leaf-level data in videos, though it appears incremental as it builds on existing segmentation and tracking methods.
The paper tackles the problem of joint multi-leaf segmentation, alignment, and tracking from fluorescence plant videos to enable leaf-level photosynthetic analysis, achieving effectiveness, efficiency, and robustness as shown in experimental results.
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.