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Fuzzy Index to Evaluate Edge Detection in Digital Images.

ABSTRACT: In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard's index (JI) and Dice's coefficient (DC). These metrics compare two images, the first one is the reference edges image, and the second one is the detected edges image. It is important to mention that all existing metrics must binarize images before their evaluation. Binarization step causes information to be lost because an incomplete image is being evaluated. In this paper, we propose a fuzzy index (FI) for edge evaluation that does not use a binarization step. In order to process all detected edges, images are represented in their fuzzy form and all calculations are made with fuzzy sets operators and fuzzy Euclidean distance between both images. Our proposed index is compared to the most used metrics using synthetic images, with good results.

SUBMITTER: Perez-Ornelas F

PROVIDER: S-EPMC4483257 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Fuzzy Index to Evaluate Edge Detection in Digital Images.

Perez-Ornelas Felicitas F Mendoza Olivia O Melin Patricia P Castro Juan R JR Rodriguez-Diaz Antonio A Castillo Oscar O

PloS one 20150626 6

In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard's index (JI) and Dice's coefficient (DC). These metrics compare two images, the first one is the reference edges image, and the second one is the detected edges image. It is important to mention that all existing metrics must binarize images before their evaluation. Binarization step causes information to be lost because an incomplete image is being evaluated ...[more]

PMID: 26115362

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