HarrisZ: Harris Corner Selection for Next-Gen Image Matching Pipelines
arXiv:2109.12925 · doi:10.1016/j.patrec.2022.04.022
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.
References in corpus (8)
- Image Matching across Wide Baselines: From Paper to Practice
- DISK: Learning local features with policy gradient
- LoFTR: Detector-Free Local Feature Matching with Transformers
- D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
- SIFT Matching by Context Exposed
- Improving the HardNet Descriptor
- AdaLAM: Revisiting Handcrafted Outlier Detection
- D2D: Keypoint Extraction with Describe to Detect Approach