title: Fast linear feature detection using multiple directional non-maximum suppression.
abstract: The capacity to detect linear features is central to image analysis, computer vision and pattern recognition and has practical applications in areas such as neurite outgrowth detection, retinal vessel extraction, skin hair removal, plant root analysis and road detection. Linear feature detection often represents the starting point for image segmentation and image interpretation. In this paper, we present a new algorithm for linear feature detection using multiple directional non-maximum suppression with symmetry checking and gap linking. Given its low computational complexity, the algorithm is very fast. We show in several examples that it performs very well in terms of both sensitivity and continuity of detected linear features.
authors.affiliation: CSIRO Mathematical and Information Sciences, Locked Bag 17, North Ryde, New South Wales 1670, Australia. changming.sun@csiro.au
authors.forename: C
authors.initials: C
authors.lastname: Sun
authors.forename: P
authors.initials: P
authors.lastname: Vallotton
mesh_terms: D000465:Algorithms; D000818:Animals; D003878:Dermatoglyphics; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016501:Neurites / Q000648:ultrastructure; D010363:Pattern Recognition, Automated / Q000379:methods*; D018515:Plant Leaves / Q000648:ultrastructure; D018517:Plant Roots / Q000648:ultrastructure; D012680:Sensitivity and Specificity