This poses particular challenges for performance evaluation for two reasons. The system under test is an entire package implementing several algorithms rather than a single processing stage. In addition the robust techniques used make system behaviour too difficult to study at the system level. The approach taken is to assess reliability and precision performance of individual processing stages. These are quantified terms of image characteristics such as signal to noise ration and edge scale.
All techniques are quite insensitive to inhomogeneous and accelerated motion.
The differential method, however, is biased by noise while the tensor method is not biased by noise. Under certain conditions, the phase method is insensitive against global illumination changes. This paper argues that the difficulties in implementing computer vision algorithms are a major reason for the lack of research into algorithm comparison. We conclude that it is important to represent algorithms in form of reusable code . Since current vision systems do not fulfill all requirements we must pose on reusable implementation, we propose to solve the reuse problem by applying generic programming .