Classifying image analysis techniques from their output
Facultad de Ciencias Matemáticas, Complutense University, Plaza de las Ciencias 3, Madrid, 28040, Spain.
Facultad de Ciencias Matemáticas, Complutense University, Plaza de las Ciencias 3, Madrid, 28040, Spain.
Facultad de Estudios Estadísticos, Complutense University, Av. Puerta de Hierro s/n, Madrid, 28040, Spain.
Instituto de Geociencias IGEO (CSIC-UCM), Plaza de las Ciencias 3, Madrid, 28040, Spain.
- DOI
- 10.1080/18756891.2016.1180819How to use a DOI?
- Keywords
- Image segmentation; image classification; edge detection; fuzzy sets; machine learning; graphs
- Abstract
In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.
- Copyright
- © 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - C Guada AU - D Gómez AU - JT. Rodríguez AU - J Yáñez AU - J Montero PY - 2016 DA - 2016/04/01 TI - Classifying image analysis techniques from their output JO - International Journal of Computational Intelligence Systems SP - 43 EP - 68 VL - 9 IS - Supplement 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2016.1180819 DO - 10.1080/18756891.2016.1180819 ID - Guada2016 ER -