While evaluating the performance of image processing algorithms, the starting point is often the acquired image. However, in practice, several factors, extrinsic to the actual algorithm, affect its performance. These factors depend largely on the features of the acquisition system. This paper focuses on some of the key factors that affect algorithm performance, and attempts to provide some insight into defining “optimal” system features for best performance.
The system features studied in depth in the paper are camera type, camera SNR, pixel size, bit-depth and system illumination. We were primarily interested in determining the effect of each of these factors on system performance. Towards this end, we designed an experiment to measure performance on a precision measurement system using several different cameras under varying illumination settings. From the results of the experiment, we observed that the variation in performance was greater for the same algorithm under different test system configurations, than for different algorithms under the same system configuration. Using these results as the basis, we discuss at length the combination of features that contributes to an optimal system configuration for a given purpose. We expect this work to have relevance to researchers in all areas of image processing who want to optimize the performance of their algorithms when ported to actual systems.