Detection of Curvilinear Structures by Tensor Voting Applied to Fiber Characterization

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%.