TL;DR
This paper introduces a machine learning-based corner detector that is faster and more repeatable than existing methods, capable of processing live video at real-time speeds with high accuracy.
Contribution
It presents a new heuristic and a machine learning approach for corner detection, optimizing for speed and repeatability, and provides a comprehensive comparison with existing detectors.
Findings
The new detector processes live PAL video using less than 5% of processing time.
It significantly outperforms existing detectors in repeatability on 3D scene tests.
Machine learning enhances both speed and quality of corner detection.
Abstract
The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is importand because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations [Schmid et al 2000]. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection, and using machine learning we derive a feature detector from this which can fully process live PAL video using less than 5% of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize the detector, allowing it to be optimized for repeatability, with little…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
