Image gradient histogram's fitting and calculation. Issue 1 (18th January 2018)

• Record Type: Journal Article
• Authors: Ding, Chang; Dong, Lili; Xu, Wenhai
• Is Part Of: Journal of engineering. Volume 2018:Issue 1(2018)
• Page Start: 45
• Page End: 48
• Publication Date: 2018-01-18
• Subjects: image enhancement -- gradient methods -- gamma distribution -- edge detection -- expectation‐maximisation algorithm; image gradient histogram -- Gamma distribution model -- Gamma mixture model -- EM algorithm -- gradient magnitude distribution -- Canny edge‐detector; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2017.0406
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.