ELSED: Enhanced Line SEgment Drawing
This addresses the need for efficient local feature detection in real-time computer vision, though it is incremental as it builds on existing line segment detection methods.
The authors tackled the problem of fast line segment detection for real-time computer vision applications, presenting ELSED as the fastest detector in the literature with the highest repeatability in experiments on public benchmarks.
Detecting local features, such as corners, segments or blobs, is the first step in the pipeline of many Computer Vision applications. Its speed is crucial for real-time applications. In this paper we present ELSED, the fastest line segment detector in the literature. The key for its efficiency is a local segment growing algorithm that connects gradient-aligned pixels in presence of small discontinuities. The proposed algorithm not only runs in devices with very low end hardware, but may also be parametrized to foster the detection of short or longer segments, depending on the task at hand. We also introduce new metrics to evaluate the accuracy and repeatability of segment detectors. In our experiments with different public benchmarks we prove that our method accounts the highest repeatability and it is the most efficient in the literature. In the experiments we quantify the accuracy traded for such gain.