Open AccessImage gradient histogram's fitting and calculation
Author(s) -
Ding Chang,
Dong Lili,
Xu Wenhai
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0406
Subject(s) - histogram , artificial intelligence , image (mathematics) , computer science , histogram matching , computer vision , image histogram , pattern recognition (psychology) , image processing , image texture
The gradient is an important property in an image. According to the characteristics of image gradient histogram (image gradient magnitude distribution), Gamma distribution model is near to the actual distribution, so Gamma mixture model is used to fit natural image gradient distribution. First, an image can be divided into edge region and non‐edge region on the aspect of the gradient; the authors assume that each region obeys sub‐Gamma distribution with different parameters. Then, expectation maximisation (EM) algorithm is used to estimate the parameters of each part. Finally, the accuracy of the fitting result of the entire gradient distribution is verified by the correlation coefficient and the validity of the estimated gradient magnitude distribution of non‐edge region and edge region is verified by the edge‐detection experiment with different threshold. This work can select Canny edge‐detector high threshold adaptively, which can improve algorithm automatic level.