ComputerVision

Why computer vision so difficult
In part, it is because vision is an inverse problem, in which we seek to recover some unknowns given insufficient information to fully specify the solution.

The forward models that we use in computer vision are usually developed in physics (radiometry, optics, and sensor design) and in computer graphics. Both of these fields model how objects move and animate, how light reflects off their surfaces, is scattered by the atmosphere, refracted through camera lenses (or human eyes), and finally projected onto a flat (or curved) image plane.

Because so much of computer vision involves the solution of inverse problems or the estimation of unknown quantities, we need to give a heavy emphasis on algorithms, especially those that are known to work well in practice. For many vision problems, it is all too easy to come up with a mathematical description of the problem that either does not match realistic real-world conditions or does not lend itself to the stable estimation of the unknowns. What we need are algorithms that are both robust to noise and deviation from our models and reasonably efficient in terms of run-time resources and space