Edge detection and feature extraction by non-orthogonal image expansion for optimal discriminative SNR

Expansion matching (EXM) optimizes a novel matching criterion called discriminative signal-to-noise ratio (DSNR) and robustly recognizes templates under conditions of noise, severe occlusion and superposition. A family of optimal DSNR edge detectors is introduced based on the expansion filter for any given edge model. Experimental comparisons show that the authors´ step expansion filter (SEF) yields better results than the Canny edge detector (CED) in terms of DSNR, even under adverse noise conditions. As for segmentation quality, the SEF also yields higher figures of merit than the CED over a wide range of noise levels. Experiments on real images reveal that the SEF yields less noisy edge elements and preserves structural details accurately. EXM is also effective for feature extraction