Machine vision: 2D collages
[Audio Version]
Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision.
I've been nursing the idea that it's not necessary to have a detailed sense of how far away things in an image are. It's probably sufficient, in some basic contexts, to just know that one thing is in front of another and not care about absolute distances. It seems some MV researchers have gone ape over telling exactly how far away an apple on a table is using lasers, stereo displacement, and all sorts of tricks. Maybe just knowing how big an apple typically is is good enough for telling how far away it is.
When I think about 3D vision in this context, I have been likening the visible world to a collage of 2D images. Take the scene seen by a stationary camera looking at a road as cars go by. One could take the unchanging background as one image. A car moving by would be the only object of interest. What's interesting is that the image of the car, from snapshot to snapshot, doesn't change much. It's as though one just took the previous image of the car and stretched and warped it a little in order to get the current image of the car. That "smooth morphing" idea is at the heart of this 2D collage analogy.
In the car example, it should be fairly easy to use the conventional technique of seeing pixel differences between a before and after image to isolate the car from the background. Not sure yet how to deal with the morphing. It seems, fair, though, to assume that the car doesn't just disappear unless it's heading out of the scene. Instead, it should suffice to take the "before car" and place it in the "after car" space and then scale it to fit the blob. Then comes a comparison step to see how the two car images differ. Perhaps key points - edges or corners - can be found and their positions corresponded.
Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision.
I've been nursing the idea that it's not necessary to have a detailed sense of how far away things in an image are. It's probably sufficient, in some basic contexts, to just know that one thing is in front of another and not care about absolute distances. It seems some MV researchers have gone ape over telling exactly how far away an apple on a table is using lasers, stereo displacement, and all sorts of tricks. Maybe just knowing how big an apple typically is is good enough for telling how far away it is.
When I think about 3D vision in this context, I have been likening the visible world to a collage of 2D images. Take the scene seen by a stationary camera looking at a road as cars go by. One could take the unchanging background as one image. A car moving by would be the only object of interest. What's interesting is that the image of the car, from snapshot to snapshot, doesn't change much. It's as though one just took the previous image of the car and stretched and warped it a little in order to get the current image of the car. That "smooth morphing" idea is at the heart of this 2D collage analogy.
In the car example, it should be fairly easy to use the conventional technique of seeing pixel differences between a before and after image to isolate the car from the background. Not sure yet how to deal with the morphing. It seems, fair, though, to assume that the car doesn't just disappear unless it's heading out of the scene. Instead, it should suffice to take the "before car" and place it in the "after car" space and then scale it to fit the blob. Then comes a comparison step to see how the two car images differ. Perhaps key points - edges or corners - can be found and their positions corresponded.
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