This paper presents a novel image thresholding algorithm, termed as random spatial sampling and majority voting based image thresholding algorithm (RMIT). RMIT firstly obtains a population of binary subimages by using random spatial sampling and Otsu’s thresholding algorithm [1]. Then RMIT aggregates these binary subimages into a consensus binary image by majority voting technique. Since the su...

This paper compares the KH and KJ sign images in the context of the zero-thresholding at single and multiple scales. It points out that consistent zero-thresholding of curvatures remains necessary for multiple scale surface segmentation. Even though KJ sign image is a good choice for single scale surface segmentation with a zero-thresholding formula J = K , the KH sign image is a better choice ...

Segmentation is an important preprocessing step that directly affects the success in image processing applications. There are many methods and approaches used for segmentation process. Thresholding a frequently approach among these methods. several suggested to thresholding. In this study, six different thresholding were as fitness functions using moth flame algorithm results obtained from comp...

Wavelet thresholding generally assumes independent, identically distributed normal errors when estimating functions in a nonparametric regression setting. VisuShrink and SureShrink are just two of the many common thresholding methods based on this assumption. When the errors are not normally distributed, however, few methods have been proposed. In this paper, a distribution-free method for thre...

Topological methods, including persistent homology, are powerful tools for analysis of high-dimensional data sets but these methods rely almost exclusively on thresholding techniques. In noisy data sets, thresholding does not always allow for the recovery of topological information. We present an easy to implement, computationally efficient pre-processing algorithm to prepare noisy point cloud ...

We consider testing for two-sample means of high dimensional populations by thresholding. Two tests are investigated, which are designed for better power performance when the two population mean vectors differ only in sparsely populated coordinates. The first test is constructed by carrying out thresholding to remove the non-signal bearing dimensions. The second test combines data transformatio...