When I use ACO to detect image edges, there are more than 9 parameters need to be set. According to the previous methods, I chose an experimental technology to determine these parameters, but this is not rigorous due to the lack of mathematical derivation. How can I remedy this deficiency? Thank you!
The problem is suited for a genetic algorithm,
with the parameters as the genome.
The tricky part is evaluation of fitness.
For a sample image, create a black-white line image
where the lines are black pixels and all other
is white. You can obtain this BW image manually
(precision is not so much an issue) or with another
edge finding algorithm.
In order to evaluate an ant (its fitness),
let it start on the original image, possibly
at a place where the BW image has a black pixel,
meaning there's a line. Now let the ant follow
the edge. How well this is done can be measured.
For example, there must always be a black pixel
of the BW image within the scope of the ant
(say 5*5 area), and the ant must move away from
pixels it had already visited. Except at the
end of an edge where it should stop.
Regards,
Joachim
This is a good question with more than one possible answer.
In addition to the incisive answer already given by Joachim, consider
K. Socha, Ant Colony Optimization for Continuous and Mixed-Variable Domains, Ph.D. thesis, Universite Libre de Bruxelles:
https://iridia.ulb.ac.be/~mdorigo/HomePageDorigo/thesis/phd/SochaPhD.pdf
See Section 2.3.1, starting on page 33, on discrete and continuous parameters in many industrial applications. For example, see formula (3.1) on page 42. The pheromone parameter is consider in detail in Section 3.2, starting on page 44. For me, this parameter is one of the most interesting one in ant colony optimization. See formula (3.2) on page 45 for a good discussion about the relative importance of pheromone vs. heuristic information. See also the evaporation rate parameter in formula (3.3) on page 46. A thorough presentation of other ACO parameters is also given in this thesis.
This is one paper where we comapared Genetic algorithm and ACO algorithm to implement a meta heuristic optimisation applied in electronic circuit design.
It would be usefull to clarify how to use ACO in any application like image processing.
good luck;
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