Corrections to “Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)”

Bedir Bedir Yousif,Nehal Fawzy,Mohamed Maher Ata,Marwa Obaya

doi:10.1109/access.2021.3064459

Abstract

The intention of this correction is to better explain how the published article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> relates to the article <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , which was referenced in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> but without sufficient delineation.

Highlights

Corresponding author: Mohamed Maher Ata. The intention of this correction is to better explain how the published article [1] relates to the article [3], which was referenced in [1] but without sufficient delineation
Whereas the intensity value of the pixel (c, d) is R (c, d), its local mean value represented by R(c, d) which is acquired by means of average filtering with mask size = 3 × 3, and Rmin, Rmax, λ(c, d), λmin, λmax have been represented in detail in [3], [5]
Where ENS has been represented with details in [3], [5] This operation has been utilized using the β-mean which improves truth (T) subset until the entropy of the indeterminate (I) subset decreased and remains constant with the appropriate number of trials. (β-mean) operation for the neutrosophic image RNS has been represented according to the following citations [3], [5], [7], [8]: RNS (β) = R(T (β), I (β), F(β)), (4)

Summary

Introduction

The intention of this correction is to better explain how the published article [1] relates to the article [3], which was referenced in [1] but without sufficient delineation. A. OPTIMIZED NEUTROSOPHIC K-MEANS The full mathematical representation of the neutrosophic process has been discussed briefly according to the following citations [2]–[21]. F (c, d) = Rmax − R(c, d) = 1 − T (c, d) , (2) Rmax − Rmin

Results

Conclusion