I've long been interested in applying my background in functional analysis (especially wavelets) and other related areas to actually create something with "real world" value (not that I don't enjoy math for it's own sake) and I thought that computer vision would be a natural place to start until I discovered that many of the books I found were littered with geometry. Not that there is anything wrong with geometry, I just find that it isn't my cup of tea. Thus, I am curious as to whether there any serious approaches to computer vision that do not make use of geometry (projective or otherwise)? Just to keep this from getting closed, please don't turn this into a debate on whose approach is better etc.
2026-03-25 21:48:40.1774475320
Is there any approach to computer vision that doesn't make use of geometry?
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It depends on what exactly you want to achieve. The reason most of the programs contain a lot of geometry is that they try to process images from a 3D point of view, and the reality of 3D arrangement and motion of objects is best described in a geometric way. However, there might be problems where there is no need to use geometry. One example that comes to mind is optical character recognition.