By experiments on image matching under scaling transformations on a poster dataset with 12 posters with multi-view matching over scaling transformations up to a scaling factor of 6 and viewing direction variations up to a slant angle of 45 degrees with local image descriptors defined from reformulations of the pure image descriptors in the SIFT and SURF operators to image measurements in terms of Gaussian derivative operators (Gauss-SIFT and Gauss-SURF) instead of original SIFT as defined from an image pyramid or original SURF as defined from Haar wavelets, it was shown that scale-space interest point detection based on the unsigned Hessian feature strength measure formula_129 allowed for the best performance and better performance than scale-space interest points obtained from the determinant of the Hessian formula_138. Both the unsigned Hessian feature strength measure formula_129, the signed Hessian feature strength measure formula_130 and the determinant of the Hessian formula_141 allowed for better performance than the Laplacian of the Gaussian formula_142. When combined with scale linking and complementary thresholding on formula_136, the signed Hessian feature strength measure formula_132 did additionally allow for better performance than the Laplacian of the Gaussian formula_145.