Threshold regression models are useful for identifying subgroups with heterogeneous parameters. The conventional threshold split the sample based on a single and observed variable, which enforces point to be equal all of population. In this article, we consider more flexible single-index model in quantile setup, is linear combination predictors. We propose new estimator by smoothing indicator f...

Interpolating the missing traces of regularly or irregularly sampled seismic record is an exceedingly important issue in the geophysical community. Many modern acquisition and reconstruction methods are designed to exploit the transform domain sparsity of the few randomly recorded but informative seismic data using thresholding techniques. In this paper, to regularize randomly sampled seismic d...

Thresholding is a simple but effective technique for image segmentation. In this paper, a general locally adaptive thresholding method using neighborhood processing is presented. The method makes use of local image statistics of mean and variance within a variable neighborhood and two thresholds obtained from the global intensity distribution. It can thus take advantage of local thresholding an...

The study presented in this article focuses on comparative analysis of Segmentation techniques. Segmentation techniques are applied to extract Region of Interest (ROI) from medical images obtained from different medical scanners such as Ultrasound, CT or MRI. Global thresholding, Adaptive Thresholding, Region grow and Active contour using level set techniques has been used in the proposed segme...

There are several images that do not have uniform brightness which pose a challenging problem for image enhancement systems. As histogram equalization has been successfully used to correct for uniform brightness problems, a histogram equalization method that utilizes human visual system based thresholding(human vision thresholding) as well as logarithmic processing techniques were introduced la...

Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are intr...