Adaptive Single Image Superresolution Approach Using Support Vector Data Description

Adaptive Single Image Superresolution Approach Using Support Vector Data Description

An adaptive single image superresolution (SR) method using a support vector data description (SVDD) is presented. The proposed method represents the prior on high-resolution (HR) images by hyperspheres of the SVDD obtained from training examples and reconstructs HR images from low-resolution (LR) observations based on the following schemes. First, in order to perform accurate reconstruction of ...

Topic Distillation using Support Vector Data Description

Ellipse Support Vector Data Description

This paper presents a novel Boundary-based approach in one-class classification that is inspired by support vector data description (SVDD). The SVDD is a popular kernel method which tries to fit a hypersphere around the target objects and of course more precise boundary is relied on selecting proper parameters for the kernel functions. Even with a flexible Gaussian kernel function, the SVDD cou...

Subspace Support Vector Data Description

This paper proposes a novel method for solving oneclass classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data descript...

Nonparametric Control Chart Using Density Weighted Support Vector Data Description

In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution...