BACKGROUND Colour image segmentation is fundamental and critical for quantitative histological image analysis. The complexity of the microstructure and the approach to make histological images results in variable staining and illumination variations. And ultra-high resolution of histological images makes it is hard for image segmentation methods to achieve high-quality segmentation results and ...

slope stability analysis is an enduring research topic in the engineering and academic sectors. accurate prediction of the factor of safety (fos) of slopes, their stability, and their performance is not an easy task. in this work, the adaptive neuro-fuzzy inference system (anfis) was utilized to build an estimation model for the prediction of fos. three anfis models were implemented including g...

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...

In 2009, Yu et al. proposed a multimod al probability model (MPM) for clustering. This paper makes advanced clustering constructions on the MPM. We first reconstruct most existing clustering algorithms, such as the k-means, fuzzy c-means, possibilistic c-means, mean shift, classification maximum likelihood, and latent class methods, by establishing the relationships between these clustering alg...

Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our metho...