Experimental performance characterization of adaptive filters

Adaptive filters and image enhancement techniques have repeatedly been suggested to make feature extraction more robust. Few comparative analysis exists between competing techniques and even the existing ones evaluate only the effect of the filter on the image but not its effect on a feature extraction process. We present an experimental approach for the evaluation of low level vision system components in a system framework. Our method is applied to adaptive filters. By analyzing the system architecture we chose an evaluation level and derive the evaluation criteria and parameter control strategies. The results show that none of the tested techniques performs better than linear filters or the Canny edge detector