About CoralMe

Coral me is a collaborative open source system dedicated to underwater coral reef image annotation.

Global warming and local anthropogenic stressors are causing severe stress to coral reefs across the world. To take appropriate action decision makers need accurate data over large spatio-temporal scales.

The speed of data collection has increased tremendously in recent years, and as a result, millions of images are collected each years across the world. The analysis process remains painfully slow as manual inspection of each photo is often required by trained experts.

This project sets for itself the following goals

  • Facilitate the transition from research to operational
  • Introduce new computer vision tools for efficient image annotation
  • Empower the population to take part in saving our coral reefs

What is CoralMe ?

CoralMe is more than a bundle of state-of-the-art code. It aims to be a platform alloying computer vision researchers to easily experiment, improve and contribute to coral-reef monitoring all in the comfort of their usual environment. It gives researchers worldwide, the very marine biologists documenting and investigating the health of marine life, unprecedented access to the most recent advances in computer vision without having to transition to new sets of tools. It finally offers the opportunity for anyone to actively participate in monitoring the health of coral reefs across the oceans.

Why isn’t CoralMe a stand-alone software?

There are many annotation platforms and software available and it is difficult and costly for a group to transition from one tool to another. A new software is simply not the solution. CoralMe allows you to keep your tools that have been finely tuned over the years, while still benefiniting from new technology at a low cost.

See the GitHub project or our pre-print paper for more information.

CoralMe includes segmentation, feature extraction and machine learning algorithms written and published by researchers and the Open-Source community. All functionalities have previously been used in coral reef image annotation research projects across the world­. These include Superpixels, Local binary patterns, Textons, Support vector machines, Deep convolutional neural network, etc. Future innovations will be readily available as their integration into CoralMe is designed to be simple.
Up to now, implementing a state-of-the-art method into a functional tool required skilled software engineers, computer vision experts, a lot of time, and in some cases, over-priced softwares. CoralMe eliminates the need for all this. Any organization interested in enhancing their own software with new functionalities can do so with nothing more than a basic MATLAB license and a novice programmer.

This is made possible by a client-server architecture allowing simple remote procedure calls (RPCs) to the CoralMe MATLAB server. The CoralMe server is built on Open-Source technology and uses the standard JSON-RPC protocol supported by all modern languages, including .NET, C, C++, Java, Javascript, PHP, Python, Scala, etc.

The CoralMe server runs on any operating system, and can run either on your local machine or on a powerful gpu-accelerated server shared between multiple users, as it also supports concurrent sessions.
We plan to integrate CoralMe into webtools to allow people from all over the world to contribute to the coral reef monitoring effort. More information on this project soon!

Supported Methods

Screencast Demo

Manual annotation is tedious, frustrating and prone to error... And yet very much necessary. We understand that. Much of our recent development has been towards facilitating this process and making sure it fulfills the needs of both on-the-ground researcher and Machine Learning practicioner to complete a self-enhancing and functionning loop of collaboration.

This short video aims to show what a handful of passionate individual can do.

Coming soon..

Featured Samples

Meet the Team

Jean-Nicola Blanchet

Lead developer
Jean received his Master of Engineering degree at ETS in Montreal, Canada working on the automatic annotation of coral reef images. He is a technology enthusiast always seeking ways to make people’s lives easier through technology.

Sebastien Dery

Data Scientist
Involved in this project since its early days, his research spans multiple overlapping areas of machine learning and neuroscience. A scuba diver at heart, he is dedicated to contribute his part in protecting the world’s oceans.

Jacques-Andre Landry

Scientific advisor
Jacques is a professor at has been working on the automation of benthic monitoring for the last 20 years. An avid sailor, he is now serving as mentor for this project while navigating the seven seas.