Edges in grayscale digital imagery are detected by localizing the zero crossings of filtered data. To achieve this objective, truncated time- or frequency-sampled forms (FSFs) of the Laplacian-of-Gaussian (LOG) filter are employed in the transform domain. Samples of the image are transformed using the discrete symmetric cosine transform prior to adaptive filtering and the isolation of zero crossings. This paper evaluates an adaptive blockwise filtering procedure, based on the FSF of the LOG filter, which diminishes the edge localization error, emphasizes the signal-to-noise ratio around the edge, and extends easily to higher dimensions. Theoretical expressions and applications to digital images are presented.