Image analysis and computer vision is the extraction of meaningful information from digital images. One of the most prominent application of computer vision software is in medical image processing - extraction of information for the purpose of making a medical diagnosis. It can be detection and measurement of tumors, arteriosclerosis or other malign changes or it can be identifying and counting cells, etc. Other main areas are industrial machine vision (automatic quality inspection, robotics, etc) and the military (missile guidance, battlefield awareness, etc).

The science of computer vision consists of an abundance of image analysis methods. These methods have been developed over the years for solving various but often narrow image analysis tasks. The result is that these methods are very task specific and seldom can be applied to a broad range of applications.

Our conclusion is then that as a discipline computer vision lacks a solid mathematical foundation.

Our long term goal is to design a comprehensive computer vision system “from first principles”. These principles come initially from one of the most fundamental fields of mathematics, topology. The idea is that just as mathematics rests on topology (and algebra), computer vision should be built on a firm topological foundation.