  Due to its role in many computer vision tasks, image matching has been
subjected to an active investigation by researchers, which has lead to better
and more discriminant feature descriptors and to more robust matching
strategies, also thanks to the advent of the deep learning and the increased
computational power of the modern hardware. Despite of these achievements, the
keypoint extraction process at the base of the image matching pipeline has not
seen equivalent progresses. This paper presents HarrisZ$^+$, an upgrade to the
HarrisZ corner detector, optimized to synergically take advance of the recent
improvements of the other steps of the image matching pipeline. HarrisZ$^+$
does not only consists of a tuning of the setup parameters, but introduces
further refinements to the selection criteria delineated by HarrisZ, so
providing more, yet discriminative, keypoints, which are better distributed on
the image and with higher localization accuracy. The image matching pipeline
including HarrisZ$^+$, together with the other modern components, obtained in
different recent matching benchmarks state-of-the-art results among the classic
image matching pipelines. These results are quite close to those obtained by
the more recent fully deep end-to-end trainable approaches and show that there
is still a proper margin of improvement that can be granted by the research in
classic image matching methods.
