Automated analysis of leaf venation patterns

Jinane Mounsef, Lina Karam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Visual imaging methods have been lately extensively used in applications that are targeted to understand and analyze botanical patterns. There is a rich literature on imaging applications in the above field and various techniques have been developed. In this paper, we introduce a fully automated imaging approach for extracting spatial vein pattern data from leaf images, such as vein densities but also vein reticulation (loops) sizes and shapes. We applied this method to quantify leaf venation patterns of the first rosette leaf of Arabidopsis thaliana throughout a series of developmental stages. In particular, we characterized the size and shape of vein network reticulations, which enlarge and get split by new veins as a leaf develops. For this purpose, the approach mainly uses skeletonization along with other known imaging techniques in an automatic interactive way that enables the user to batch process a high throughput of data.

Original languageEnglish (US)
Title of host publicationIEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIVI 2011
Subtitle of host publication2011 IEEE Workshop on Computational Intelligence for Visual Intelligence
Pages1-5
Number of pages5
DOIs
StatePublished - 2011
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Workshop on Computational Intelligence for Visual Intelligence, CIVI 2011 - Paris, France
Duration: Apr 11 2011Apr 15 2011

Publication series

NameIEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIVI 2011: 2011 IEEE Workshop on Computational Intelligence for Visual Intelligence

Other

OtherSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Workshop on Computational Intelligence for Visual Intelligence, CIVI 2011
CountryFrance
CityParis
Period4/11/114/15/11

Keywords

  • adaptive thresholding
  • edge detection
  • feature extraction
  • leaf venation
  • skeletonization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

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