Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

Zhen Zheng,He Zhang,Yanliang Gao,Youshi Xuchen,Bingting Zha,Hailu Yuan

doi:10.1109/access.2019.2927655

Zhen Zheng, He Zhang + Show 4 more

Open Access

https://doi.org/10.1109/access.2019.2927655

Copy DOI

Abstract

The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the 3×3 neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the 3×3 neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).

Highlights

The ‘‘edge’’ of an image is the region which shows the most dramatic shift in gray level [1]
ACTUAL IMAGE EDGE DETECTION To verify the edge detection effects of the proposed algorithm, we compared it against state-of-the-art methods including the Canny operator, improved Canny algorithm proposed by
Rong et al [7], morphological method proposed by Fu and Jiang [21], Zernike moment method proposed by Peng et al [15], improved Sobel operator proposed by Shi et al [8], and the traditional grey relation analysis (GRA) proposed by

Summary

INTRODUCTION

The ‘‘edge’’ of an image is the region which shows the most dramatic shift in gray level [1]. Z. Zheng et al.: Adaptive Edge Detection Algorithm Based on Gray Entropy Theory and Textural Features. Ma first used grey relation analysis (GRA) for image edge detection in 2003 [29], where the algorithm did reveal edge information accurately and with some anti-noise capability. Li proposed an effective edge detection algorithm based on GRA and validated it by simulation [30], but its anti-noise ability is still weak. A novel, adaptive edge detection algorithm based on grey entropy theory and textural features was designed in this study. The geometric relation degree model reflects the closeness of the reference sequence and comparison sequence according to the proximity of each element in the sequence.

GREY ENTROPY THEORY

EXPERIMENTAL RESULTS

CONCLUSION