access icon openaccess Image gradient histogram's fitting and calculation

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.

Inspec keywords: gradient methods; expectation-maximisation algorithm; image enhancement; gamma distribution; edge detection

Other keywords: EM algorithm; gradient magnitude distribution; Gamma distribution model; image gradient histogram; Gamma mixture model; Canny edge-detector

Subjects: Image recognition; Other topics in statistics; Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Other topics in statistics

http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2017.0406
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content/journals/10.1049/joe.2017.0406
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