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<h1>About CoralNet</h1>
    <p>CoralNet is a repository and a resource for benthic images analysis. It was created as part of an NSF project,
    <a href="#funding">Computer Vision Coral Ecology</a> by researchers at UCSD in 2012. The site implements sophisticated
    computer vision algorithms that allow researchers, agencies, and private partners to rapidly annotate benthic survey
    images. The site also serves as a repository and collaboration platform for the scientific community.</p>
<h2>Background</h2>
    <p>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, thanks to proliferation of digital cameras and underwater
    autonomous, or remotely operated, vehicles. As a result, millions of images are collected each years across the
    world. The subsequent analysis, however, remains painfully slow as manual inspection of each photo is required
    by a trained expert.</p>
    <p>Recent progress in machine learning and computer vision offer a compelling solution to this problem: a
    machine-annotator can learn from human labels and then proceed to rapidly process large numbers of new images.</p>
    <p>CoralNet offers an open-source framework to approach this solution. It serves as a repository of benthic images,
    and allow for different modes of interaction between the machine-annotator and the human expert. CoralNet, by it’s
    nature, also provides a platform for collaboration / sharing of data.</p>
<h2>Data Agreement</h2>
    <p>CoralNet are committed to protecting the data privacy of it’s users, and offer two privacy options which are set
    of the <a href="#source">Source</a> level.</p>
<h3>Private</h3>
    <p>
        All your images and annotations are hidden from the public. Your project will however appear in the source
        listing and map, so that other researchers / interested parties can find you. The only information we will share
        is thus:
    </p>
    <p>1. Name of source</p>
    <p>2. Source description and affiliation</p>
    <p>3. Latitude and Longitude of your sites</p>
    <p>4. Total number of images in your <a href="#source">source</a></p>
    <p>
        All other information remains hidden from the public. CoralNet also reserves the right to let the computer
        vision algorithm learn from annotation made on private sources. This will in no way compromise the integrity of
        your data, but it will help further boost the efficiency of the machine-annotator for all users.
        Note: According to the US National Science Foundation guidelines on data policy, all scientific information
        should be made publicly available two years after collection. We therefore encourage users to switch their
        privacy setting from Private to Public when appropriate.
    </p>
<h3>Public</h3>
    <p>
        In addition to the information shared in the private mode, all your source images and annotation data will be
        available for the public to browse and download (including original images in full resolution). We encourage all
        users to consider this privacy option.
    </p>
<h2>The site</h2>
    <p>
        The CoralNet website consist of several modules:
    </p>
    <p>
        <a name="source"></a>
        <b>Source creation:</b> Your “source” is your space at CoralNet. You specify what labelset you want to use, your
        privacy settings, and invite collaborators to your source. The source interface also allows you to organize your
        images by a number of locationkeys, such as “site”, “habitat”, “depth”.
    </p>
    <p>
        <b>Labelset:</b> Specify what labels you want to use in your analysis. Choose from a set of already created
        labels or create your own if needed.
    </p>
    <p>
        <b>Import:</b> Upload images to the server. You can also import annotation already completed in other systems
        such as Coral Point Count.
    </p>
    <p>
        <b>Annotation:</b> Annotate your image right in the web browser using a point count interface. CoralNet
        supports manual annotation, as well as semi-automated modes. Several users can annotate the same set of images
        at the same time, and the system tracks who annotated what. After each annotation session, the CoralNet machine
        annotator will read your annotation and improve its prediction on your next batch of images.
    </p>
    <p>
        The computer vision algorithm used by CoralNet was developed in the following publication: “Automatic
        Annotation of Coral Reef Survey Images”, <NAME>. and <NAME>. and <NAME>. and <NAME>.
        and <NAME>., Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, Rhode Island 2012.
    </p>
    <p>
        <b>Statistics:</b> Get statistics detailing the coverage of each category in your labelset along with
        aggregated functional group level statistics.
    </p>
    <p>
        <b>Export:</b> Export all images and annotations to your desktop for backup or further processing.
    </p>
<a name="funding"></a>
<h2>Funding support</h2>
    <p>
        CoralNet development is support by the NSF Computer Vision Coral Ecology grant #ATM-0941760. We ask that you cite the following paper if you have used CoralNet in your work
    </p><p>
    <NAME>., <NAME>., <NAME>., <NAME>.,<NAME>., "Automated Annotation of Coral Reef Survey Images", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, June, 2012.
    </p>


<h2>Contact</h2>
<p>
    Contact us by filling out this <a href="{% url contact %}">form</a>.
</p>


<h2>Affiliation</h2>

<h3>Development team</h3>
    <table class="people">
        <tr>
            <td><img width="150" src="../static/img/mugshots/oscar-beijbom.jpg"></td>
            <td>
                <b><NAME> - Project Manager</b>
                <p>
                    Previously lead developer of the <a href="http://www.hovding.com ">Hovding invisible bicycle
                    helmet</a>, Oscar is now a fourth year PhD student at the
                    computer science department at University of California, San Diego. His research focus on automating the scientific
                    analysis of reef survey images, using elements from texture recognition, color analysis, and machine learning.
                    <a href="http://vision.ucsd.edu/~beijbom/website/">http://vision.ucsd.edu/~beijbom/website/</a>
                </p>
            </td>
        </tr>
        <tr>
            <td></td>
            <td>
                <p><b><NAME> - Lead Developer</b></p>
                <p><b><NAME></b></p>
                <p><b><NAME></b></p>
            </td>
        </tr>
        <tr>
            <td><img width="150" src="../static/img/mugshots/jeff-sandvik.png"></td>
            <td>
                <b><NAME></b>
                <p>
                    Jeff is a fourth-year Computer Science undergraduate at University of California, San Diego.
                </p>
            </td>
        </tr>
    </table>
<h3>Advisory team</h3>
    <table class="people">
        <tr>
            <td><img width="150" src="../static/img/mugshots/david-kriegman.png"></td>
            <td>
                <b><NAME></b>
                <p><commit_msg>Update about page<commit_after>{% extends "base.html" %}

{% block content %}

<style>
table.people td {
    vertical-align: middle;
    padding: 10px
}
</style>

<h1>About CoralNet</h1>
    <p>CoralNet is a repository and a resource for benthic images analysis. It was created as part of an NSF project,
    <a href="#funding">Computer Vision Coral Ecology</a> by researchers at UCSD in 2012. The site implements sophisticated
    computer vision algorithms that allow researchers, agencies, and private partners to rapidly annotate benthic survey
    images. The site also serves as a repository and collaboration platform for the scientific community.</p>
<h2>Background</h2>
    <p>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, thanks to proliferation of digital cameras and underwater
    autonomous, or remotely operated, vehicles. As a result, millions of images are collected each years across the
    world. The subsequent analysis, however, remains painfully slow as manual inspection of each photo is required
    by a trained expert.</p>
    <p>Recent progress in machine learning and computer vision offer a compelling solution to this problem: a
    machine-annotator can learn from human labels and then proceed to rapidly process large numbers of new images.</p>
    <p>CoralNet offers an open-source framework to approach this solution. It serves as a repository of benthic images,
    and allow for different modes of interaction between the machine-annotator and the human expert. CoralNet, by it’s
    nature, also provides a platform for collaboration / sharing of data.</p>
<h2>Data Agreement</h2>
    <p>CoralNet are committed to protecting the data privacy of it’s users, and offer two privacy options which are set
    of the <a href="#source">Source</a> level.</p>
<h3>Private</h3>
    <p>
        All your images and annotations are hidden from the public. Your project will however appear in the source
        listing and map, so that other researchers / interested parties can find you. The only information we will share
        is thus:
    </p>
    <p>1. Name of source</p>
    <p>2. Source description and affiliation</p>
    <p>3. Latitude and Longitude of your sites</p>
    <p>4. Total number of images in your <a href="#source">source</a></p>
    <p>
        All other information remains hidden from the public. CoralNet also reserves the right to let the computer
        vision algorithm learn from annotation made on private sources. This will in no way compromise the integrity of
        your data, but it will help further boost the efficiency of the machine-annotator for all users.
        Note: According to the US National Science Foundation guidelines on data policy, all scientific information
        should be made publicly available two years after collection. We therefore encourage users to switch their
        privacy setting from Private to Public when appropriate.
    </p>
<h3>Public</h3>
    <p>
        In addition to the information shared in the private mode, all your source images and annotation data will be
        available for the public to browse and download (including original images in full resolution). We encourage all
        users to consider this privacy option.
    </p>
<h2>The site</h2>
    <p>
        The CoralNet website consist of several modules:
    </p>
    <p>
        <a name="source"></a>
        <b>Source creation:</b> Your “source” is your space at CoralNet. You specify what labelset you want to use, your
        privacy settings, and invite collaborators to your source. The source interface also allows you to organize your
        images by a number of locationkeys, such as “site”, “habitat”, “depth”.
    </p>
    <p>
        <b>Labelset:</b> Specify what labels you want to use in your analysis. Choose from a set of already created
        labels or create your own if needed.
    </p>
    <p>
        <b>Import:</b> Upload images to the server. You can also import annotation already completed in other systems
        such as Coral Point Count.
    </p>
    <p>
        <b>Annotation:</b> Annotate your image right in the web browser using a point count interface. CoralNet
        supports manual annotation, as well as semi-automated modes. Several users can annotate the same set of images
        at the same time, and the system tracks who annotated what. After each annotation session, the CoralNet machine
        annotator will read your annotation and improve its prediction on your next batch of images.
    </p>
    <p>
        The computer vision algorithm used by CoralNet was developed in the following publication: “Automatic
        Annotation of Coral Reef Survey Images”, <NAME>. and <NAME>. and <NAME>. and <NAME>.
        and <NAME>., Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, Rhode Island 2012.
    </p>
    <p>
        <b>Statistics:</b> Get statistics detailing the coverage of each category in your labelset along with
        aggregated functional group level statistics.
    </p>
    <p>
        <b>Export:</b> Export all images and annotations to your desktop for backup or further processing.
    </p>
<a name="funding"></a>
<h2>Funding support</h2>
    <p>
        CoralNet development is support by the NSF Computer Vision Coral Ecology grant #ATM-0941760. We ask that you cite the following paper if you have used CoralNet in your work
    </p><p>
    <NAME>., <NAME>., <NAME>., <NAME>.,<NAME>., "Automated Annotation of Coral Reef Survey Images", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, June, 2012.
    </p>


<h2>Contact</h2>
<p>
    Contact us by filling out this <a href="{% url contact %}">form</a>.
</p>


<h2>Affiliation</h2>

<h3>Development team</h3>
    <table class="people">
        <tr>
            <td><img width="150" src="../static/img/mugshots/oscar-beijbom.jpg"></td>
            <td>
                <b><NAME> - Project Manager</b>
                <p>
                    Previously lead developer of the <a href="http://www.hovding.com ">Hovding invisible bicycle
                    helmet</a>, Oscar is now a fourth year PhD student at the
                    computer science department at University of California, San Diego. His research focus on automating the scientific
                    analysis of reef survey images, using elements from texture recognition, color analysis, and machine learning.
                    <a href="http://vision.ucsd.edu/~beijbom/website/">http://vision.ucsd.edu/~beijbom/website/</a>
                </p>
            </td>
        </tr>
        <tr>
            <td></td>
            <td>
                <p><b><NAME> - Lead Developer</b></p>
                <p><b><NAME></b></p>
            </td>
        </tr>
        <tr> 
            <td><img width="150" src="../static/img/mugshots/andrew-hu.jpg"></td>    
            <td>            
                <b><NAME></b>
                <p>Andrew is a third-year Computer Science undergratuate at the University of California, San Diego.</p>
            </td>
            
	</td>
        <tr>
            <td><img width="150" src="../static/img/mugshots/jeff-sandvik.png"></td>
            <td>
                <b><NAME></b>
                <p>
                    Jeff is a fourth-year Computer Science undergraduate at University of California, San Diego.
                </p>
            </td>
        </tr>
    </table>
<h3>Advisory team</h3>
    <table class="people">
        <tr>
            <td><img width="150" src="../static/img/mugshots/david-kriegman.png"></td>
            <td>
                <b><NAME></b>
                <p>
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