Algorithm improvement for Coca-Cola can shape recognition

Post by tocomment » Thu, Jan 03, 2013, 11:10 PM UTC

strebler wrote: post #8

tocomment wrote: post #3

This computer vision stuff is so fascinating. Does anyone know what the career prospects are like if you specialize in this? Is there much consulting demand? What kind of fees do CV consultants charge?

The original post really highlights the "problem with computer vision" - finding the coke can is fairly easy (SIFT), although the author goes off on some other random approach that I doubt would work very well. The field certainly has its share of Do-It-Yourselfers, plus it's also quite common to outsource labour overseas. As for fees, it's entirely reflective of a) how much money the problem is costing the client an…

Post by strebler » Sun, Jan 06, 2013, 8:36 PM UTC

buq2 wrote: post #10

strebler wrote: post #8

Earlier quoted context omitted.

The original post really highlights the "problem with computer vision" - finding the coke can is fairly easy (SIFT), although the author goes off on some other random approach that I doubt would work very well. The field certainly has its share of Do-It-Yourselfers, plus it's also quite common to outsource labour overseas. As for fees, it's entirely reflective of a) how much money the problem is costing the client an…

I don't see any problem with the original solution. It is classic (but little bit old fashioned) way of detecting objects, far from random. I also think that you oversimplify the SIFT solution. You use sift, then what? Match the feature vectors, get bunch of possible correspondence points, prune outliers using 3D model of a cylinder (or assume that the face of the can is flat plane and just use homography) and robust…