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<p>In a recent paper by J.S. Chen et al. the authors presented a means of decomposing the Laplacian-of-Gaussian (LoG) kernel into the product of a Gaussian and a (smaller) LoG mask. They then proceeded to develop a fast algorithm for convolution which exploits the spatial frequency properties of these operators to allow the image to be decimated (subsampled). Although this approach is both novel and interesting, it is contended that the exposition suffers from some inconsistencies and minor errors. The commenters clarify matters for those who wish to implement this technique. The original authors acknowledge two of the three points raised, and provide further clarification of the other one namely, the claim that the masks (Gaussian and LoG) are too small.</p>
fast kernel; convolution; decimation; subsampling; Laplacian-of-Gaussian masks; pattern recognition; picture processing

K. Boyer and G. Sotak, Jr., "Comments, with Reply, on 'Fast Convolution with Laplacian-of-Gaussian Masks' by J.S. Chen et al," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 1329-1332, 1989.
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