Machine Vision and Applications: Computer Vision and Image Analysis in Plant Phenotyping Call for Papers

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CALL FOR PAPERS
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Machine Vision and Applications
Special Issue on
Computer Vision and Image Analysis in Plant Phenotyping
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Website: http://www.plant-phenotyping.org/CVPPP2014-Special-Issue/

Important Dates 
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Submission:         April 20 2015
First decisions:    June 20 2015
Revision deadline:  July 30 2015
Final decisions:    Aug 30 2015
Online publication: November 2015

Scope
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Plant phenotyping is the identification of effects on the phenotype
(i.e., the plant appearance and behavior) as a result of genotype
differences (i.e., differences in the genetic code) and the
environment. Previously, the process of taking phenotypic measurements
has been manual, costly, and time consuming.  In recent years,
non-invasive, imaging-based methods have become more common. These
images are recorded by a range of capture devices from small embedded
camera systems to multi-million Euro smart-greenhouses, at scales
ranging from microscopic images of cells, to entire fields captured by
UAVs.
 
These images need to be analyzed in a high throughput, robust, and
accurate manner. UN-FAO statistics show that according to current
population predictions we will need to achieve a 70% increase in food
productivity by 2050, simply to maintain current global nutrition
levels. Phenomics -large-scale measurement of plant traits– is the
bottleneck here, and machine vision is ideally placed to
help. However, the occurring problems differ from usual tasks
addressed by the computer vision community due to the requirements
posed by this application scenario.  Dealing with these new problems
has spawned new specialized workshops such as CVPPP (Computer Vision
Problems in Plant Phenotyping) which was held for the first time in
conjunction with ECCV 2014, and the stand-alone workshop IAMPS (Image
Analysis Methods for the Plant Sciences) now in its fourth year.
 
The overriding goal of this special issue is to focus on submissions
that propose interesting computer vision solutions, but also
submissions that introduce challenging computer vision problems in
plant phenotyping accompanied with benchmark datasets and suitable
performance evaluation methods.

Specific topics of interest include, but are not limited to, the
following:

* problem statements accompanied by image data sets defining plant
phenotyping challenges, complete with annotations if appropriate, and
accompanied with benchmark methods wherever possible, and suitable
evaluation methods

* advances in segmentation, tracking, detection, reconstruction and
identification methods that address unsolved plant phenotyping
scenarios

* open source implementation, comparison and discussion of existing
methods


Submission
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Authors are encouraged to submit original work that has not appeared
in, nor is in consideration by, other journals. Previously published
conference papers can be submitted in extended form (with additional
supporting experiments and a more detailed technical description of
the method). All papers will be subject to expert peer review.

Further information on the process (as well any special issue related
updates) are available at:
http://www.plant-phenotyping.org/CVPPP2014-Special-Issue/

The electronic copy of a complete manuscript (10-15 pages in the
Machine Vision and Applications publication format
http://www.springer.com/computer/image+processing/journal/138?detailsPage=pltci_2116423)
should be submitted through the journal manuscript tracking system at
the web site:
http://www.editorialmanager.com/mvap/
indicating that the contribution is for the special issue “Computer
Vision and Image Analysis in Plant Phenotyping”.


Guest editors (alphabetical order)
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Hannah Dee, Aberystwyth University, UK (hmd1@aber.ac.uk)
Andrew French, University of Nottingham, UK (Andrew.P.French@nottingham.ac.uk)
Hanno Scharr, Forschungszentrum Jülich, Germany (h.scharr@fz-juelich.de)
Sotirios Tsaftaris, IMT Lucca, Italy (s.tsaftaris@imtlucca.it)