Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

Nicola Strisciuglio*, George Azzopardi, Nicolai Petkov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)


The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure = 0.865 ). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings
PublisherSpringer Verlag
Number of pages13
Volume10424 LNCS
ISBN (Print)9783319646886
Publication statusPublished - 2017
Event17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017 - Ystad, Sweden
Duration: 22-Aug-201724-Aug-2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10424 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017


  • Crack delineation
  • Curved lines
  • Line detection
  • Non-linear filtering

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