SuperPoint: Self-Supervised Interest Point Detection and Description

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM Workshop (DL4VSLAM2018

Performance Assessment of Feature Detection Algorithms: A Methodology and Case Study on Corner Detectors

In this paper we describe a generic methodology for evaluating the labeling performance of feature detectors. We describe a method for generating a test set and apply the methodology to the performance assessment of three well-known corner detectors: the Kitchen-Rosenfeld, Paler et al. and Harris-Stephens corner detectors. The labeling deficiencies of each of these detectors is related to their discrimination ability between corners and various of the features which comprise the class of noncorners

A novel search for gravitationally lensed radio sources in wide-field VLBI imaging from the mJIVE-20 survey

We present a novel pilot search for gravitational lenses in the mJIVE-20 survey, which observed $24\,903$ radio sources selected from FIRST with the VLBA at an angular resolution of 5 mas. We have taken the visibility data for an initial $3\,640$ sources that were detected by the mJIVE-20 observations and re-mapped them to make wide-field images, selecting fourteen sources that had multiple components separated by $\geq100$ mas, with a flux-ratio of $\leq15$:$1$ and a surface brightness consistent with gravitational lensing. Two of these candidates are re-discoveries of gravitational lenses found as part of CLASS. The remaining twelve candidates were then re-observed at 1.4 GHz and then simultaneously at 4.1 and 7.1 GHz with the VLBA to measure the spectral index and surface brightness of the individual components as a function of frequency. Ten were rejected as core-jet or core-hotspot(s) systems, with surface brightness distributions and/or spectral indices inconsistent with gravitational lensing, and one was rejected after lens modelling demonstrated that the candidate lensed images failed the parity test. The final lens candidate has an image configuration that is consistent with a simple lens mass model, although further observations are required to confirm the lensing nature. Given the two confirmed gravitational lenses in the mJIVE-20 sample, we find a robust lensing-rate of $1$:($318\pm225$) for a statistical sample of 635 radio sources detected on mas-scales, which is consistent with that found for CLASS.Comment: 31 pages, 22 figures; accepted for publication in MNRA

A micropower centroiding vision processor

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