An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique

Entropy

Qutaibeh D. Katatbeh¹  José Martínez-Aroza²  Juan Francisco Gómez-Lopera³  David Blanco-Navarro³ 

[1] Department of Mathematics and Statistics, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, JordanDepartamento de Matemática Aplicada, Universidad de Granada, Granada 18071, Spain;Departamento de Física Aplicada, Universidad de Granada, Granada 18071, Spain;

关键词: entropy;    Jensen–Shannon divergence;    image segmentation;    entropic edge detection;    multi-size window processing;   

DOI  :  10.3390/e17127858

来源: mdpi

【 摘 要 】

In this paper we develop a new procedure for entropic image edge detection. The presented method computes the Jensen–Shannon divergence of the normalized grayscale histogram of a set of multi-sized double sliding windows over the entire image. The procedure presents a good performance in images with textures, contrast variations and noise. We illustrate our procedure in the edge detection of medical images.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.