There are a lot of vision algorithms, and picking the right one isn't straightforward. It really depends on what your source data is and what you are trying to get out of it, as well as your performance limitations. If you are trying to find colored blobs without definite shapes, some things that I can think of off the top of my hand are expanding shapes (such as rectangles or circles), fitting shapes, random sampling algorithms, and weighted averages / other statistical calculations. If shape does matter, it gets more complicated. Basically, the best way to start learning about this sort of thing is just to mess around and to see what works. Reading papers is also useful for researching algorithms / strategies if you can get through the math (at least the important bits).
Here are a few pdfs from a robot competition. The code samples inside are in java, so you can ignore the topics on java-specific features (i.e. how it handles binary manipulations or performance of various ways to access image data). But the ideas are mostly pretty general (they don't go into that much depth) so it could be a good jumping off point.
http://web.mit.edu/6.186/2006/lectures/Vision.pdf
http://web.mit.edu/6.186/2005/doc/morevision.pdf
http://web.mit.edu/6.186/2007/tutori...geTutorial.pdf
To repeat, the pdfs won't help you with implementation, but they may spark some ideas / give a basic overview of a few concepts.