  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.
