Patricia Melin

Prof. Patricia Melin

Tijuana Institute of Technology, Tijuana, Baja California, Mexico
orcid

Research Focus & Metrics

Academic Career Length

33Years (1994-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2009 in Applied Soft Computing
Verified
Journal Article
Closed access
MGM.A. Porta Garcia
M.A. Porta Garcia
Oscar Humberto Montiel RossOscar Humberto Montiel Ross
Oscar Humberto Montiel Ross
Oscar CastilloOscar Castillo
Oscar Castillo
RSRoberto Sepúlveda
Roberto Sepúlveda
Patricia MelinPatricia Melin
Patricia Melin

1 April 2012 in Applied Soft Computing
Verified
Journal Article
Closed access
Oscar CastilloOscar Castillo
Oscar Castillo
Patricia MelinPatricia Melin
Patricia Melin

1 September 2014 in Information Sciences
Verified
Journal Article
Closed access
Oscar CastilloOscar Castillo
Oscar Castillo
Patricia MelinPatricia Melin
Patricia Melin

1 June 2009 in Information Sciences
Verified
Journal Article
Closed access
JCJuan Ramon Castro
Juan Ramon Castro
Oscar CastilloOscar Castillo
Oscar Castillo
Patricia MelinPatricia Melin
Patricia Melin
ARAntonio Rodriguez-Diaz
Antonio Rodriguez-Diaz

1 August 2014 in Applied Soft Computing
Verified
Journal Article
Closed access
Patricia MelinPatricia Melin
Patricia Melin
Oscar CastilloOscar Castillo
Oscar Castillo

2 January 2014 in IEEE Transactions on Fuzzy Systems
Verified
Journal Article
Closed access
Patricia MelinPatricia Melin
Patricia Melin
CLAUDIA IBETH GONZALEZ BERRELLEZACLAUDIA IBETH GONZALEZ BERRELLEZA
CLAUDIA IBETH GONZALEZ BERRELLEZA
JCJuan Ramon Castro
Juan Ramon Castro
OMOlivia Mendoza
Olivia Mendoza
Oscar CastilloOscar Castillo
Oscar Castillo
This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented. The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.