RT Journal Article
T1 A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
A1 Flores Vidal, Pablo Arcadio
A1 Villarino, Guillermo
A1 Gómez González, Daniel
A1 Montero De Juan, Francisco Javier
AB Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approachbased on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.
PB Atlantis Press
SN 1875-6883
YR 2019
FD 2019
LK https://hdl.handle.net/20.500.14352/12426
UL https://hdl.handle.net/20.500.14352/12426
LA eng
NO P. A. Flores-Vidal, G. Villarino, D. Gómez, and J. Montero, “A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms,” vol. 12, no. 1, pp. 367–378, 2019, doi: https://doi.org/10.2991/ijcis.2019.125905653.
NO Ministerio de Economía, Industria y Competitividad (España)
NO Comunidad de Madrid
DS Docta Complutense
RD 8 abr 2025