LSD: A Fast Line Segment Detector with a False Detection Control

By Grompone von Gioi Rafael, Jakubowicz Jérémie, Morel Jean-Michel, and Randall Gregory
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

  • Rafael Grompone von Gioi

    ENS Cachan



November 6, 2013

Last update

November 6, 2013










LSD is a linear-time Line Segment Detector giving subpixel accurate results. It is designed to work on any digital image without parameter tuning. It controls its own number of false detections: On average, one false alarms is allowed per image [1]. The method is based on Burns, Hanson, and Riseman's method [2], and uses an a contrario validation approach according to the Desolneux, Moisan, and Morel's theory [3,4]. The version described here includes some further improvement over the one described in [1].

Combined State and Parameter Reduction

Proceedings in Applied Mathematics and Mechanics (2014)

Himpe Christian and Ohlberger Mario

Comparing (Empirical-Gramian-Based) Model Order Reduction Algorithms

Working Paper (2020)

Himpe Christian

Normal forms and invariant manifolds for nonlinear, non-autonomous PDEs, viewed as ODEs in infinite dimensions

Working Paper (2019)

Hochs Peter and Roberts A.J.

On Empirical System Gramians

Proceedings in Applied Mathematics and Mechanics (2019)

Grundel Sara, Himpe Christian, and Saak Jens

Cross-Gramian-Based Dominant Subspaces

Advances in Computational Mathematics (2019)

Benner Peter and Himpe Christian

On Reduced Input-Output Dynamic Mode Decomposition

Advances in Computational Mathematics (2018)

Himpe Christian, Benner Peter, and Mitchell Tim

Fast Low-Rank Empirical Cross Gramians

Proceedings in Applied Mathematics and Mechanics (2017)

Himpe Christian, Leibner Tobias, Rave Stephan, and Saak Jens

emgr - The Empirical Gramian Framework

Algorithms (2018)

Himpe Christian

Cross-Gramian-Based Model Reduction: A Comparison

Modeling, Simulation and Applications (2017)

Himpe Christian and Ohlberger Mario

0 comment

Add comment

You need to log in to post a comment.