A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms

DOI

10.2991/ijcis.2019.125905653How to use a DOI?

Keywords

Image processing; Edge detection; Global evaluation; Edge segments; Supervised classification

Abstract

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 approach based 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.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Pablo A. Flores-Vidal
AU  - Guillermo Villarino
AU  - Daniel Gómez
AU  - Javier Montero
PY  - 2019
DA  - 2019/01/28
TI  - A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
JO  - International Journal of Computational Intelligence Systems
SP  - 367
EP  - 378
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2019.125905653
DO  - 10.2991/ijcis.2019.125905653
ID  - Flores-Vidal2019
ER  -