Algorithm improvement for Coca-Cola can shape recognition(stackoverflow.com) |
Algorithm improvement for Coca-Cola can shape recognition(stackoverflow.com) |
Corresponding meta discussion, mentioned in the question: http://meta.stackoverflow.com/questions/129423/on-the-bounda.... I merely wonder why it's tagged 'java'.
I personally really enjoy working with images and the projects have had good variety even if they have all been about industrial machine vision (measurement). At least in Finland demand for industrial machine vision seems to be good, but "machine vision" has still certain stigma because of previously failed projects. Many projects have failed because of lack of good hardware (cameras, illumination, computing power) and knowledge. At least the hardware side is now in quite good condition.
As for fees, it's entirely reflective of a) how much money the problem is costing the client and b) your ability to prove to them your value to the client. Clients do not always respect the complexity of the problems they're facing, so it's easier to work with those who have tried for themselves.
For what it's worth, we're hiring right now in this space (computer vision software development), located in Ottawa, Canada: http://ca.indeed.com/job/software-engineer-71a851096a104223
SIFT is faster than generalized HoughTF in this case, use of points instead of whole objects/blobs gives you nice ways to validate using 3D model, etc., but I would still not look down on some one who finds and implements this solutions as a student.
Given the constraints of the asker (all available in OpenCV), the solution I would suggest first is SIFT + homography, both easy to use and in OpenCV (sample code is around). Yes, there's a lot of other possibilities, but this would be an improvement over the original.