Contrast of a fuzzy relation

Bustince, Humberto; Barrenechea, Edurne; Fernández, Javier; Pagola, Miguel; Montero, Javier; Guerra, C.

doi:10.1016/j.ins.2009.12.013

Cited by 25 publications

(7 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, there is an algorithm that uses Epanechnikov function extended as a membership function for each class to be assigned to each pixel 64 , or another algorithm which uses operators that detect specific patterns of neighboring pixels for fuzzy rules 77 . A study about representing the dataset as a fuzzy relation, associating a membership degree with each element of the relation is presented in 78 . Other issue is that fuzzy logic can be used only on a certain part of the edge detection process 72 .…”

Section: Fuzzy Edge Detectionmentioning

confidence: 99%

Classifying image analysis techniques from their output

Guada¹,

Gómez²,

Rodríguez³

et al. 2016

IJCIS

View full text Add to dashboard Cite

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.

show abstract

Section: Fuzzy Edge Detectionmentioning

confidence: 99%