As a part of my research into occlusions I read a paper on "Motion Segmentation Using Occlusions" by Abhijit S. Ogale, Cornelia Fermuller, and Yiannis Aloimonos. I've added a link to the paper in the sidebar under
Papers and Downloads if you're interested in reading.
This paper the authors strive to create a general method for segmenting a scene into various different objects, but the section which I found most interesting was section V, in which they discussed their algorithm for determining ordinal depths (meaning like object 1 in front of object 2 in front of object 3) from three video frames. Their technique seems to me elegant in it's simplicity, although obviously the math behind it is somewhat complex.
While considering what to do for a project, I have found myself stuck on the question "What useful applications of occlusion detection are there?" The problem here is questioning when occlusion detection, which is a relatively long-winded process, is needed over a more simple solution. For example, if you want to detect whether a door is open or not, a simple laser beam will do the trick much more efficiently than a costly occlusion detection system. And if you merely want to detect when your view of a certain object is being blocked, simply looking for changes in intensity makes a lot more sense than trying to actually detect an occluding object.
So I've been able to brainstorm a small number of examples where occlusion detection would be a better solution than something computationally simple:
1. When there might be multiple occlusions occuring in a scene, and only certain ones are important (Example: a door opening may not be a problem, but someone walking through it might be).
2. When the surface being occluded is too complexly textured for a simple intensity algorithm to work (Example: Suppose you want to detect if someone is in front of your TV screen, but the screen itself may be constantly changing).
3. You care about the structure of the occluding object (Example: you have a machine which puts labels on cans. You want it to stick a label when it has a can in front of it, but not someone's hand)
These are some interesting ideas to think about, and ultimately I will rest on one of them, or a similar one, for my project. However, initially I want to get some sort of occlusion detection algorithm working. I think it will give me a better idea of when exactly occlusion can be a useful tool.
