Setting low-level vision parameters

As vision systems become more and more complex there is an increasing need to understand the interaction between the various modules that these systems are composed of. In this paper we attempt to answer the question of how a high-level module can feed back its knowledge to a low-level module to improve the performance of the overall system. In particular we consider a system model consisting of a single low-level module that takes a set of low-level parameters as input and a single high-level module that estimates a set of high-level model parameters. We consider the task of setting the low-level parameters to maximize the performance of the overall system. Previous approaches to this problem include setting the parameters by hand, empirical evaluation, learning, and updating the parameters using the previous image in a video. We propose an approach based on simultaneous optimization of the high-level and low-level parameters. After outlining the approach, we demonstrate it on three examples: (1) color-blob tracking, (2) colorbased lane tracking, and (3) edge-based lane tracking.