Unsupervised range-constrained thresholding

Three range-constrained thresholding methods are proposed in the light of human visual perception. The new methods first implement gray level range-estimation, using image statistical characteristics in the light of human visual perception. An image transformation is followed by virtue of estimated ranges. Criteria of conventional thresholding approaches are then applied to the transformed image for threshold selection. The key issue in the process lies in image transformation which is based on unsupervised estimation for gray level ranges of object and background. The transformation process takes advantage of properties of human visual perception and simplifies an original image, which is helpful for image thresholding. Three new methods were compared with their counterparts on a variety of images including nondestructive testing ones, and the experimental results show its effectiveness.

Research highlights
► It proposes an approach implementing unsupervised estimation for gray level ranges of object and background, using image statistical characteristics in the light of human visual perception.
► An image transformation is followed by virtue of estimated ranges.
► The transformation process takes advantage of properties of human visual perception and simplifies an original image, which is helpful for image thresholding.