Detection of Curvilinear Structures by Tensor Voting Applied to Fiber Characterization

Nataliya Strokina, Tatiana Kurakina, Tuomas Eerola, Lasse Lensu, Heikki Kalviainen

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

7 Citations (Scopus)


The paper presents a framework for the detection of curvilinear objects in images. Such objects are challenging to be described by a geometrical model, and although they appear in a number of applications, the problem of detecting curvilinear objects has drawn limited attention. The proposed approach starts with an edge detection algorithm after which the task of object detection becomes a problem of edge linking. A state-of-the-art local linking approach called tensor voting is used to estimate the edge point saliency describing the likelihood of a point belonging to a curve, and to extract the end points and junction points of these curves. After the tensor voting, the curves are grown from high-saliency seed points utilizing a linking method proposed in this paper. In the experimental part of the work, the method was systematically tested on pulp suspension images to characterize fibers based on their length and curl index. The fiber length was estimated with the accuracy of 71.5% and the fiber curvature with the accuracy of 70.7%.

Original languageEnglish
Title of host publicationIMAGE ANALYSIS, SCIA 2013
EditorsJK Kamarainen, M Koskela
PublisherSpringer-Verlag, Berlin
Number of pages12
ISBN (Print)978-3-642-38885-9
Publication statusPublished - 2013
Externally publishedYes
Publication typeA4 Article in conference proceedings
Event18th Scandinavian Conference on Image Analysis (SCIA) - Espoo, Finland
Duration: 17 Jun 201320 Jun 2013

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference18th Scandinavian Conference on Image Analysis (SCIA)


  • curvilinear structure segmentation
  • edge linking
  • machine vision
  • image processing and analysis
  • pulping
  • papermaking


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