scholarly journals Edge Detection Method for Latent Fingerprint Images Using Intuitionistic Type-2 Fuzzy Entropy

2016 ◽  
Vol 16 (3) ◽  
pp. 205-218 ◽  
Author(s):  
Devarasan Ezhilmaran ◽  
Manickam Adhiyaman
Keyword(s):  
Edge Detection ◽  
Distance Measure ◽  
Detection Method ◽  
Fuzzy Entropy ◽  
Latent Fingerprint ◽  
Exact Boundary ◽  
Quality Image ◽  
Edge Detection Method ◽  
Fuzzy Divergence

Abstract A latent fingerprint is an interesting issue because of it has attained from crime places and moreover contained a low quality image, less number of features and unwanted noises. It is necessity to extract the original image with exact boundary from the surface for further processing such as authentication, identification and matching. In this work, a new distance measure has been proposed for latent fingerprint edge detection using Intuitionistic Type-2 Fuzzy Entropy (IT2FE) and a comprehensible definition is made for Intuitionistic Type-2 Fuzzy Sets (IT2FS). IT2FS takes into account of uncertainty in the form of membership function which is also termed as Intuitionistic Type-2 Fuzzy Divergence (IT2FD). The experiment is conducted with public domain fingerprint databases such as FVC-2004 and IIIT-latent fingerprint. The edge detection is carried out with the proposed method and the results are discovered better regarding existing method.

An improved sobel edge detection method based on generalized type-2 fuzzy logic

2014 ◽  
Vol 20 (2) ◽  
pp. 773-784 ◽  
Author(s):  
Claudia I. Gonzalez ◽  
Patricia Melin ◽  
Juan R. Castro ◽  
Olivia Mendoza ◽  
Oscar Castillo
Keyword(s):  
Fuzzy Logic ◽  
Edge Detection ◽  
Detection Method ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Generalized Type

Image Edge Detection Based on Direction Fuzzy Entropy

2011 ◽  
Vol 268-270 ◽  
pp. 1234-1238
Author(s):  
Xian Qing Ling ◽  
Jun Lu ◽  
Lei Wang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Feature Space ◽  
Fuzzy Entropy ◽  
Noise Performance ◽  
Edge Pixel ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Image Edges

To improve the ability of the fuzzy edge detection and anti-noise performance, the paper proposes a new weighted direction fuzzy entropy image edge detection method. The proposed method converts the feature space of image gray to the fuzzy feature space, and then extracts the weighted information measure of the direction structural in the fuzzy entropy feature space. Finally, the proposed method determines the edge pixel by an adaptive threshold after non-maxima suppression. The experiment demonstrates that the proposed method can extract the image edges effectively by means of the fuzzy edge detection.

Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

2014 ◽  
Vol 22 (6) ◽  
pp. 1515-1525 ◽  
Author(s):  
Patricia Melin ◽  
Claudia I. Gonzalez ◽  
Juan R. Castro ◽  
Olivia Mendoza ◽  
Oscar Castillo
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Edge Detection ◽  
Detection Method ◽  
Edge Detection Method ◽  
Generalized Type

Edge detection method based on Interval type-2 fuzzy systems for color images

2015 ◽  
Author(s):  
C. I. Gonzalez ◽  
J. R. Castro ◽  
O. Mendoza ◽  
A. Rodriguez-Diaz ◽  
P. Melin ◽  
...  
Keyword(s):  
Edge Detection ◽  
Fuzzy Systems ◽  
Detection Method ◽  
Color Images ◽  
Edge Detection Method ◽  
Interval Type

scholarly journals General Type-2 Fuzzy Sugeno Integral for Edge Detection

2019 ◽  
Vol 5 (8) ◽  
pp. 71
Author(s):  
Gabriela E. Martínez ◽  
Claudia I. Gonzalez ◽  
Olivia Mendoza ◽  
Patricia Melin
Keyword(s):  
Edge Detection ◽  
Figure Of Merit ◽  
General Type ◽  
Detection Method ◽  
General Process ◽  
Sugeno Integral ◽  
Morphological Gradient ◽  
Edge Detection Method ◽  
Image Gradients

A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients in the four directions—horizontal, vertical, and the two diagonals—and this technique is known as the morphological gradient. After that, the general type-2 fuzzy Sugeno integral (GT2 FSI) is used to integrate the four image gradients. In this second step, the GT2 FSI establishes criteria to determine at which level the obtained image gradient belongs to an edge during the process; this is calculated assigning different general type-2 fuzzy densities, and these fuzzy gradients are aggregated using the meet and join operators. The gradient integration using the GT2 FSI provides a methodology for achieving more robust edge detection, even more if we are working with blurry images. The experimental evaluations are performed on synthetic and real images, and the accuracy is quantified using Pratt’s Figure of Merit. The results values demonstrate that the proposed edge detection method outperforms other existing algorithms.

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