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Official Round: Completed

ImageCLEF 2022 Coral - Annotation and Localisation

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Note: ImageCLEF 2022 Coral includes 2 subtasks. This page is about the Annotation and Localisation subtask. For information about the Pixel-wise Parsing subtask click here. Both challenges' datasets are shared together, 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. Pressing the red Participate button leads you to a page where you have to agree with those rules. You will not be able to submit any results before agreeing with the rules.

Note: Before trying to submit results, read the Submission instructions section on this page.

Challenge description

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.

New for 2022:

Previous editions of ImageCLEFcoral in 2019 and 2020 have shown improvements in task performance and promising results on cross-learning between images from geographical regions. The 3rd edition in 2021 increased the complexity of the task and size of data available to participants through supplemental data, resulting in lower performance than previous years. The 4th edition plans to address these issues by targeting algorithms for geographical regions and raising the benchmark performance. As with the 3rd edition, the training and test data will form the complete set of images required to form 3D reconstructions of the marine environment. This will allow the participants to explore novel probabilistic computer vision techniques based around image overlap and transposition of data points.

This challenge (subtask) requires the participants to label the images with types of benthic substrate together with their bounding box in the image. Each image is provided with possible class types. For each image, participants will produce a set of bounding boxes, predicting the benthic substrate for each bounding box in the image.

Data


As soon as the data are released they will be available under the "Resources" tab.


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. The images partially overlap with each other and can be used to create 3D photogrammetric models of the marine environment.

Substrates of the same type can have very different morphologies, coloUr 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 colour balance due to the cameras being used. This is representative of the Marine Technology Research Unit dataset and all images are useful for data analysis. The training set used for 2022 has undergone a significant review in order to rectify errors in classification and polygon shape. Additionally, the 13 substrate types have been refined to help participants understand the results of their analyses.

The training set contains images from 4 locations. These images are grouped into image sets that can be used to create a 3D model of the enviroment using photogrammetry and partially overlap. The test set contains images froma single location (K1, Kaledupa, Indonesia) so particpants can choose whoch sets to train their systems with. 

 

Class

Description

Examples

Algae - Macro or Leaves

Leafy or bulbous structures that can also overgrow other benthic substrates. Fine (grass-like) turf algae is not included. Typically vibrant green.

Sponge

Includes encrusting, leafy, tubular, boulder-like, vase and chimney morphologies that can appear in a variety of colours. Often have a “rough” looking surface from spicules and small holes.

 

 

Sponge – Barrel

Includes all large barrel-sponge shaped species such as Xestospongia muta, but also includes young, small barrel sponges.

 

Hard Coral – Foliose

Leaf-like or cabbage-like leaf structures

 

Hard Coral – Table

Circular, broad horizontal forms originating from a single, thick stem. Polyps on the edge appear lighter.

  

Hard Coral – Mushroom

Individual corals, either circular or oval shaped.

 

Hard Coral – Branching

Numerous branches with secondary branching. Includes plate corals such as Elk Horn coral. Can grow in bushes similar to Table Coral but rounded at the top (not flat).

 

Hard Coral – Submassive

Digitate or pillar forms growing upwards from a thick stem. Includes small, packed finger-like structures and thick branching structures without secondary branching.

 

Fire Coral – Millepora

Fine branching structures similar to branching coral. Very few substrates were in the dataset and were hard to distinguish from Hard Coral -Branching so this category is not used.

Not used

Hard Coral – Boulder

Boulder-like corals with polyps arranged evenly across the surface. Includes thin, hard encrusting type corals.  

 

Hard Coral – Encrusting

Fleshy or boulder-like structures with polyps arranged in channels rather than individually. Includes brain corals, rose corals and bubble corals.

 

 

Soft Coral

A wide range of morphologies from clumped, branching types (that can be confused with branching coral) to lobed structures. Can have a fleshy, soft appearance.

 

 

Soft Coral – Gorgonian

Sea fans (thin vertical branching plates from a single stem) and sea whips (long, thin soft coral from a single stem).

 

 

The training set contains images from 4 locations. These images are grouped into image sets that can be used to create a 3D model of the enviroment using photogrammetry and partially overlap. The test set contains images froma single location (K1, Kaledupa, Indonesia) so particpants can choose whoch sets to train their systems with. 

Image set

Location

Similarity to test set

# images

K1-20180712-01

K1, Kaledupa, Indonesia

Same location

173

PK-20180714-01

PK, Hoga Indonesia

Similar location (within 10 miles)

244

PK-20180729-02

PK, Hoga Indonesia

Similar location (within 10 miles)

270

20180406-spermonde-keke

Keke, Spermonde Archipelago, Indonesia

Geographically and ecologically similar

266

20190417-seychelles-BL

Curieuse Island, Seychelles

Geographically distinct but ecologically similar

120

20170803-dominica-cabrits

Cabrits, Dominica, Caribbean 

Geographically and ecologically distinct

301

 

 

3D models for ImageCLEFcoral 2022

The images for each model was collected using a 5-camera array moving over the terrain. The images typically overlap each other by 60% and are likely to contain some of the same features of the landscape taken from many different angles. The images were aligned using Agisoft Metashape and processed into a 3D textured model using "medium" processing settings. The models are available to participants on request (as .obj files).

In addition, participants are encouraged to use the publicly available NOAA NCEI data and/or CoralNet to train their approaches. The CNETcategories_ImageCLEF_v1.xlsx file shows how to map NOAA categories to ImageCLEFcoral categories for training. NB: NOAA data is typically sparse pixel annotation over a large set of images, i.e, only 10 pixels per images are classified.

Submission instructions


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


Before being allowed to submit your results, you have to first press the red participate button, which leads you to a page where you have to accept the challenge's rules.


Rules


Note: In order to participate in this challenge you have to sign an End User Agreement (EUA). You will find more information on the 'Resources' tab.


ImageCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2022. CLEF 2022 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here.

Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by ImageCLEF organizing committee to ensure quality. As an illustration, ImageCLEF 2021 working notes (task overviews and participant working notes) can be found within CLEF 2021 CEUR-WS proceedings.

Important

Participants of this challenge will automatically be registered at CLEF 2022. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:

  • First name

  • Last name

  • Affiliation

  • Address

  • City

  • Country

  • Regarding the username, please choose a name that represents your team.

This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs

Participating as an individual (non affiliated) researcher

We welcome individual researchers, i.e. not affiliated to any institution, to participate. We kindly ask you to provide us with a motivation letter containing the following information:

  • the presentation of your most relevant research activities related to the task/tasks

  • your motivation for participating in the task/tasks and how you want to exploit the results

  • a list of the most relevant 5 publications (if applicable)

  • the link to your personal webpage

The motivation letter should be directly concatenated to the End User Agreement document or sent as a PDF file to bionescu at imag dot pub dot ro. The request will be analyzed by the ImageCLEF organizing committee. We reserve the right to refuse any applicants whose experience in the field is too narrow, and would therefore most likely prevent them from being able to finish the task/tasks.

Citations

Information will be posted after the challenge ends.

Prizes

Publication

ImageCLEF 2022 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.

Resources

Contact us

Discussion Forum

Alternative channels

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 :

  • jchamb [at] essex [dot] ac [dot] uk
  • alba [dot] garcia [at] essex [dot] ac [dot] uk
  • alien [at] essex dot] ac [dot] uk
  • a [dot] campello [at] wellcome [dot] ac [dot] uk

More information

You can find additional information on the challenge here: https://www.imageclef.org/2022/coral

Participants