How to quantify area of interest in an image?
Given that the size of the barnacles is not expected to be considerably larger than the area of the hull being imaged at a certain shot, you can simply deduce the actual barnacle coverage area by counting how many pixels belong to the hull and then multiplying by a "pixel size" that you can derive by including an item of known length in the image.

The key assumption behind this (simple) technique is that the barnacles and the hull exist on the same plane (As if the barnacle layer was a "sticker" on the hull). 

Obviously, this is not the case because the barnacles are three dimensional and "stick out" of the hull. For the same reason, the angle at which a certain part of the hull is imaged might also introduce an additional error factor. 

You can control for all this by imaging relatively large areas of the hull (i.e. the camera is at a relatively long distance from the hull), taking care for the camera to be pointing vertically to as much of the hull as it is possible (this of course conflicts with the "take a picture at a long distance" and some experimentation will be required). Finally, you will also need to have good control of the lighting of the shot and for this reason, you might have to use a flash bulb (even in day-light) to flood the scene uniformly with light and avoid shadows.

Having taken the image, you can then convert its colour space from Red, Green, Blue (RGB) to Hue, Saturation, Brightness (HSB) and examine the Hue component's histogram for the uniform colour of the hull (here, Red and because of this, relatively easy to pick out). Again, this assumes that the hull is painted using just one colour.

Here is the process in Gimp:



The three small B&amp;W images at the botoom of the screenshot are the HSB components respectively. The histogram at the top right is derived from the H component (the first B&amp;W image at the bottom left-hand side of the screenshot).

As you can see, there is a clear peak towards the beginning of the H spectrum corresponding to the red colour (Please see the H band in this image to understand how the horizontal axis of the histogram now "corresponds" to hue value).

If there was an object of known length on the hull (an iron waterline marking relief for exaple or a pen or a coin) we could derive the pixel size and through this, the actual surface of the whole image. By then counting how many pixels "belong" to the peak we could deduce the actual surface occupied by the barnacles.

For example:

Image Dimensions: $450 \cdot 229 = 103050$ pixels

Pixels corresponding to red: $8494$

(Please note:You probably cannot see this because of the size of the image but once you have your histogram in GIMP (Colours->Info->Histogram) you can mark an area on the histogram and read how many pixels it describes. This is how this number was derived.).

Barnacle coverage area (%): $\frac{103050-8494}{103050} * 100 \approx 91.7\% $

All you need now is an object of known size to convert this to $cm^2$

In terms of automating this process, it can be done using a number of techniques and platforms (e.g. Python, Octave, etc) but for a more accurate answer reagrding those, a little bit more detail would be required regarding the application.

Hope this helps.