An Unsupervised Algorithm for Detecting Good Continuation in Dot Patterns
José Lezama, Gregory Randall, Jean-Michel Morel, Rafael Grompone von Gioi
→ BibTeX
@article{ipol.2017.176,
    title   = {{An Unsupervised Algorithm for Detecting Good
Continuation in Dot Patterns}},
    author  = {Lezama, José and Randall, Gregory and Morel, Jean-Michel and Grompone von Gioi, Rafael},
    journal = {{Image Processing On Line}},
    volume  = {7},
    pages   = {81--92},
    year    = {2017},
    doi     = {10.5201/ipol.2017.176},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2017.176}}
published
2017-04-24
reference
José Lezama, Gregory Randall, Jean-Michel Morel, and Rafael Grompone von Gioi, An Unsupervised Algorithm for Detecting Good Continuation in Dot Patterns, Image Processing On Line, 7 (2017), pp. 81–92. https://doi.org/10.5201/ipol.2017.176

Communicated by Julie Digne
Demo edited by José Lezama

Abstract

In this article we describe an algorithm for the automatic detection of perceptually relevant configurations of 'good continuation' of points in 2D point patterns. The algorithm is based on the 'a contrario' detection theory and on the assumption that 'good continuation' of points are locally quasi-symmetric. The algorithm has only one critical parameter, which controls the number of false detections.

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