Performance analysis for a class of iterative image thresholding algorithms

A performance analysis procedure that analyses the properties of a class of iterative image thresholding algorithms is described. The image under consideration is modeled as consisting of two maximum-entropy primary images, each of which has a quasi-Gaussian probability density function. Three iterative thresholding algorithms identified to share a common iteration architecture are employed for thresholding 4595 synthetic images and 24 practical images. The average performance characteristics including accuracy, stability, speed and consistency are analysed and compared among the algorithms. Both analysis and practical thresholding results are presented.

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