Multi-leaf tracking from fluorescence plant videos

Xi Yin, Xiaoming Liu, Jin Chen, David M. Kramer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations


Driven by the plant phenotyping application, this paper proposes a new leaf tracking framework to jointly segment, align and track multiple leaves from fluorescence plant videos. Our framework consists of two steps. First, leaf alignment is applied to one video frame to generate a collection of leaf candidates. Second, we define a set of transformation parameters operated on the leaf candidates in order to optimize the alignment in the subsequent video frame according to an objective function. Gradient descent is employed to solve this optimization problem. Experimental results show that the proposed multi-leaf tracking algorithm is superior to the image-based leaf alignment method in terms of three quantitative metrics.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Number of pages5
ISBN (Electronic)9781479957514
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014


  • Leaf tracking
  • alignment
  • multi-leaf

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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