Detecting transparent glass in images

Are there any methods in the computer vision literature that allows for detecting transparent glass in images? Like if I have an image of a car, can I detect windows? etc...

All methods I've found so far are active methods (i.e. require calibration, control over the environment or lasers). I nee开发者_如何学Pythond a passive method (i.e. all you have is an image, or multi-view images of the object and thats it).

Here is some very recent work aimed at detecting transparent objects in a general setting.

http://books.nips.cc/papers/files/nips22/NIPS2009_0397.pdf

http://videolectures.net/nips09_fritz_alfm/

I think what you looking for is detection of translucent regions. There is very limited work here since it is a very hard problem. Basically it is a major chicken and egg problem. Translucent regions cause almost all fundamental image processing tools to fail (e.g. motion estimation, feature matching, tracking, etc...). Yet you must use such tools to detect translucent regions. Anyway, up to my knowledge this is the most recent piece of work in this area and I doubt there is any other.

http://www.mee.tcd.ie/~sigmedia/pmwiki/uploads/Misc.Icip2011/CVPR_new.pdf

It is published in CVPR which is a top conference in Computer Vision.

Just a wild guess: if the camera is moving and you perform a 3D reconstruction of the scene, you could detect large discontinuities of the reconstructions at the reflected regions.

I think you should provide a clearer description of what your are trying to achieve.
The paper "Deriving intrinsic images from image sequences" shows some results with transparencies.
If you are close enough, you may be able to use the glass refraction (a la Snell's law) to detect the glass from multiple views.
I also think that reflections (specular regions) are a good indication for curved glasses.

Detecting it is one thing, but separating is another. You can do separation because its like putting 2 sounds with 1 of the sounds 180 degree out of phase. If you manage to learn the phasing sound by itself, you have the other sound automatically, so you could then learn that one too. Im stuck at the point where I can only superimposesubtract them if I learnt them by themselves. So the real gain here is somehow learning this addup, as 2 separate things, even though you never saw them apart.