HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines

TL;DR

HarrisZ$^+$ enhances keypoint detection in image matching by refining the HarrisZ detector, leading to more discriminative, well-distributed, and accurately localized keypoints, achieving state-of-the-art results among classic pipelines.

Contribution

The paper introduces HarrisZ$^+$, an improved HarrisZ corner detector optimized for modern image matching pipelines, with refined selection criteria and parameter tuning.

Findings

HarrisZ$^+$ outperforms previous detectors in benchmarks.

The pipeline achieves results close to deep learning approaches.

Classic methods still hold significant potential for improvement.

Abstract

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.