Moon image segmentation with a new mixture histogram model

ABSTRACT

This paper proposes an algorithm named Histogram Mixture Model Genetic algorithm combining histograms and mixture models to segment images. The histogram of an image is approximated by several Gaussian functions with parameters. The parameters are obtained by the genetic algorithm with a fitness function defined by an error function. The algorithm improves the drawbacks of Otsu and non-parametric methods. In the experiments, the algorithm has better results than Matlab function graythresh for Otsu method as compared to the theoretical optimal value. The experimental results of moon images show that the hole in Marius Hills is segmented.

KEYWORDS:

• Image segmentation
• genetic algorithm
• mixture models
• histogram segmentation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Shenzhen Development and Reform Commission through Shenzhen Development and Reform [Grant (2016) 889]; Shenzhen Government Grant [JCYJ20160531191837793, KQJSCX20170726104033357].