To advance the state of the art in leaf segmentation and to demonstrate the difficulty of segmenting all leaves in an image of plants, we organize the Leaf Segmentation and Counting Challenges (LSC and LCC). This is the 3rd LSC after the successful LSC 2014 and 2015 and the 2nd LCC. Examples of methods stemming from these challenges or using the data are http://link.springer.com/article/10.1007/s00138-015-0737-3 , https://arxiv.org/abs/1605.09410 , https://arxiv.org/abs/1511.08250 ). The major difference of this years challenge is the expansion of the data that we focus on leaf segmentation accuracy and as such ground truth foreground segmentation masks are provided for training and testing.
For the challenges we release training sets (containing raw images and annotations) and testing sets (containing raw images, only). Papers will be evaluated and ranked according to their outcome, the validity of the algorithm, and suitability of the approach. Only fully automated approaches will be accepted. Accepted papers will be presented either orally or in a poster session, and will appear in the proceedings. Should a large, high quality, number of papers be received, a collation study, summarizing algorithms and results, will be presented instead with authors presenting details in a poster session.
A jointly authored paper presenting the findings of the collation study may be invited to a high impact journal in computer vision (to be announced at the workshop if appropriate). The collation paper will be compiled primarily from participants presenting at the workshop.
Please read first the challenge terms and conditions
We share images of tobacco plants and arabidopsis plants. Tobacco images were collected using a camera which contained in its field of view a single plant. Arabidopsis images were collected using a camera with a larger field of view encompassing many plants, which were cropped. The images released are either from mutants or wild types and have been taken in a span of several days. Plant images are encoded as tiff files.
All images were hand labelled to obtain ground truth masks for each leaf in the scene. These masks are image files encoded in PNG where each segmented leaf is identified with a unique integer value, starting from 1, where 0 is background. For the counting problem, annotations are provided in the form of a png image where each leaf center is denoted by a single pixel. Additionally a CSV file with image name and number of leaves is provided.
For further information on the ground truth annotation process, please refer to:
Hanno Scharr, Massimo Minervini, Andreas Fischbach, Sotirios A. Tsaftaris. Annotated Image Datasets of Rosette Plants. Technical Report No. FZJ-2014-03837, Forschungszentrum Jülich, 2014
|Challenge opens for registration||May 2017|
|Data ready for download||May 2017|
|Submit results on testing data||June 20 2017, 11:59PM Pacific Time|
|Evaluation of results on testing data||June 23 2017|
|Paper submission deadline||June 28 2017, 11:59PM Pacific Time|
|Notification of acceptance||Aug 10 2017|
|Camera-ready paper||Aug 24 2017, 11:59PM Pacific Time|
|Author registration deadline||Aug 24 2017|
|Workshop day||Oct 28 2017|
Registration stays open till June 19 2017.
Sotirios A. Tsaftaris, University of Edinburgh, UK
Hanno Scharr, IBG-2, Forschungszentrum Jülich, Germany