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Classifying image analysis techniques from their output

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Gauda, C. and Gomez, D. and Rodríguez, Juan Tinguaro and Yáñez, Javier and Montero, Javier (2016) Classifying image analysis techniques from their output. International Journal of Computational Intelligence Systems, 9 (Sup. 1). pp. 43-68. ISSN 1875-6883

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Official URL: http://www.tandfonline.com/doi/abs/10.1080/18756891.2016.1180819?journalCode=tcis20


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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.


Item Type:Article
Additional Information:

Special Issue: A Humble Tribute to 50 Years of Fuzzy Sets, guested edited by Luis Martínez and Jie Lu

Uncontrolled Keywords:Image segmentation; Image classification; Edge detection; Fuzzy sets; Machine learning; Graphs.
Subjects:Sciences > Mathematics > Logic, Symbolic and mathematical
ID Code:38173
Deposited On:23 Jun 2016 08:22
Last Modified:24 Jun 2016 08:19

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