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 is the most efficient in the literature and quantify the accuracy traded for such gain.
翻译:检测本地特征,如角、区块或浮筒等,是许多计算机视野应用程序进入管道的第一步,其速度对于实时应用至关重要。在本文中,我们介绍了文献中最快的线段探测器ELSED。其效率的关键是局部段增长算法,在小的不连续情况下连接梯度对齐像素。拟议的算法不仅运行在极低端硬件的设备中,而且可能进行对称,以促进短端或长端段的探测,视手头的任务而定。我们还引入新的计量标准,以评价区段探测器的准确性和可重复性。在用不同的公共基准进行的实验中,我们证明我们的方法在文献中最为高效,并量化交易的准确性,以获取这种收益。