Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

A fuzzy edge-weighted centroidal Voronoi tessellation model for image segmentation

Image representation using Voronoi tessellation

The major approaches to image representation [l] in terms of its constituent regions may be divided into two broad categories: (1) those which specify the borders of the regions and (2) those which describe their interiors. Most of the approaches and the more interesting ones, belong to the latter category. This may be attributed to the increased dimensionality of information (regions, rather t...

Image representation using Voronoi tessellation: adaptive and secure

An image is represented by the Voronoi tessellation generated from selected sampling points. Using a multiresolution approach, the density of the sampling points can be adaptive to image properties: smoother regions will have fewer sampling points than more detailed regions. The adaptation property results in better image quality than non-adaptive Voronoi representations, while preserving the p...

Centroidal Voronoi Tessellation Algorithms for Image Processing

Centroidal Voronoi tessellations (CVT’s) are special Voronoi tessellations for which the generators of the tessellation are also the centers of mass (or means) of the Voronoi cells or clusters. CVT’s have been found to be useful in many disparate and diverse settings. In this paper, CVT-based algorithms are developed for image compression, image segmenation, and multichannel image restoration a...

An Image Segmentation Algorithm for the Hyperspectral Remote Sensing Image

The technology of hyperspectral remote sensing image improves the capability of collecting the objects such as lakes, rivers, farmlands, buildings, forest and desert in the ground surface. Since the spatial resolution is becoming higher recently, image segmentation of hyperspectral remote sensing is important to the next step of remote sensing image classification and object recognition. In thi...