Loading
AIcrowd
Challenges
Organize a challenge FAQ Contact Jobs
Log In Sign Up
Round 1: Completed

ImageCLEF 2019 Coral - Pixel-wise parsing

Hidden

By  ImageCLEF 
1989
5
0
24
Participate

Note: ImageCLEF Coral 2019 is divided into 2 subtasks (challenges). This challenge is about Annotation and Localisation. For information on the Pixel-wise Parsing challenge click here . Both challenges share the same dataset, so registering for one of these challenges will automatically give you access to the other one.

Note: Do not forget to read the Rules section on this page.

Motivation

The increasing use of structure-from-motion photogrammetry for modelling large-scale environments from action cameras attached to drones has driven the next-generation of visualisation techniques that can be used in augmented and virtual reality headsets. It has also created a need to have such models labelled, with objects such as people, buildings, vehicles, terrain, etc. all essential for machine learning techniques to automatically identify as areas of interest and to label them appropriately. However, the complexity of the images makes impossible for human annotators to assess the contents of images on a large scale. Advances in automatically annotating images for complexity and benthic composition have been promising, and we are interested in automatically identify areas of interest and to label them appropriately for monitoring coral reefs. .Coral reefs are in danger of being lost within the next 30 years, and with them the ecosystems they support. This catastrophe will not only see the extinction of many marine species, but also create a humanitarian crisis on a global scale for the billions of humans who rely on reef services. By monitoring the changes and composition of coral reefs we can help prioritise conservation efforts.

Challenge description

This task requires the participants to segment and parse each coral reef image into different image regions associated with benthic substrate types. For each image, segmentation algorithms will produce a semantic segmentation mask, predicting the semantic category for each pixel in the image.

Data

The data for this task originates from a growing, large-scale collection of images taken from coral reefs around the world as part of a coral reef monitoring project with the Marine Technology Research Unit at the University of Essex. Substrates of the same type can have very different morphologies, color variation and patterns. Some of the images contain a white line (scientific measurement tape) that may occlude part of the entity. The quality of the images is variable, some are blurry, and some have poor color balance. This is representative of the Marine Technology Research Unit dataset and all images are useful for data analysis. The images contain annotations of the following 13 types of substrates: Hard Coral – Branching, Hard Coral – Submassive, Hard Coral – Boulder, Hard Coral – Encrusting, Hard Coral – Table, Hard Coral – Foliose, Hard Coral – Mushroom, Soft Coral, Soft Coral – Gorgonian, Sponge, Sponge – Barrel, Fire Coral – Millepora and Algae - Macro or Leaves.

The training set contains contains 240 images with 6670 substrates annotated. The test set contains 200 images.

The data can be downloaded from the “Dataset” tab and will be made available on:

  • 04.02.2019 Training data

  • 18.03.2019 Test data

Submission instructions

As soon as the submission is open, you will find a “Create Submission” button on this page (just next to the tabs)

The submissions will be received through the crowdai system.

Participants will be permitted to submit up to 10 runs. External training data is allowed and encouraged.

Each system run will consist of a single ASCII plain text file. The results of each test set should be given in separate lines in the text file. The format of the text file is as follows:

[image_ID/document_ID] [results]

The results of each test set image should be given in separate lines, each line providing only up to 500 localised substrates, with up to 500 coordinate localisations of the same substrate expected. The format has characters to separate the elements, semicolon ‘;’ for the substrates, colon ‘:’ for the confidence, comma ‘,’ to separate multiple bounding polygons, and ‘x’ and ‘+’ for the size-offset bounding polygon format, i.e.:

[image_ID];[substrate1] [[confidence1,1]:][x1,1]+[y1,1]+[x2,1]+[y2,1]+….+[xn,1]+[yn,1],[[confidence1,2][x1,2]+[y1,2]+[x2,2]+[y2,2]+….+[xn,2]+[yn,2];[substrate2] …

[confidence] are floating point values 0-1 for which a higher value means a higher score and the [xi,yi] represents consecutive points.

For example, in the development set format (notice that there are 2 polygons for substrate c_soft_coral):

  • 2018_0714_112604_057 0 c_hard_coral_branching 1 1757 833 1645 705 1559 598 1442 540 1249 593 1121 679 1020 705 998 844 891 967 966 1122 1137 1143 1324 1122 1468 1074 1655 978
  • 2018_0714_112604_057 3 c_soft_coral 1 2804 1368 2745 1368 2724 1427 2729 1507 2809 1507 2825 1453
  • 2018_0714_112604_057 4 c_soft_coral 1 2697 1576 2638 1592 2638 1608 2622 1667 2654 1694 2713 1731 2777 1731 2777 1635

In the submission format, it would be a line as:

*2018_0714_112604_057;c_hard_coral_branching 0.6:1757+833+1645+705+1559+598+1442+540+1249+593+1121+679+1020+705+998+844+891+967+966+1122+1137+1143+1324+1122+1468+1074+1655+978;c_soft_coral 0.7:2804+1368+2745+1368+2724+1427+2729+1507+2809+1507+2825+1453,0.3:2697+1576+2638+1592+2638+1608+2622+1667+2654+1694+2713+1731+2777+1731+2777+1635

Citations

Information will be posted after the challenge ends.

Evaluation criteria

More on the evaluation criteria will be published soon.

Resources

Contact us

We strongly encourage you to use the public channels mentioned above for communications between the participants and the organizers. In extreme cases, if there are any queries or comments that you would like to make using a private communication channel, then you can send us an email at :

  • Jon Chamberlain <jchamp(at)essex.ac.uk>,University of Essex, UK

  • Adrian Clark <alien(at)essex.ac.uk>,University of Essex, UK

  • Antonio Campello <antonio.campello(at)filament.ai>,Filament, UK

  • Alba García Seco de Herrera <alba.garcia(at)essex.ac.uk>,University of Essex, UK

More information

You can find additional information on the challenge here: https://www.imageclef.org/2019/coral

Prizes

ImageCLEF 2019 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.

Datasets License

Participants