Voronoi Image Segmentation and Its Applications to Geoinformatics

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">As various geospatial images are available for analysis, there is a strong need for an intelligent geospatial image processing method. Segmenting and districting digital images is a core process and is of great importance in many geo-related applications. We propose a flexible image segmentation framework based on generalized Voronoi diagrams through Euclidean distance transforms. We introduce a three-scan algorithm that segments images in O(N) time when N is the number of pixels. The algorithm is capable of handling generators of complex types (point, line and area), Minkowski metrics and different weights. This paper also provides applications of the proposed method in various geoinformation datasets. Illustrated examples demonstrate the usefulness and robustness of our proposed method.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p&gt

Comparing image segmentation methods using data envelopment analysis

“…Image segmentation is the process of dividing an image into meaningful parts, while this definition can be ambiguous, it more or less states what image segmentation is all about. Image segmentation as an integral part of image processing has many applications, examples include medical sciences [1], geoinformatics [2], biomechanics [3], Multimedia [4] and etc.…”

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

“…Voronoi diagrams have been used in pattern recognition in combination with computational geometry methods and neural networks to design a classifier for the objects from a given dataset [ 22 ]. Furthermore, Voronoi diagrams are used for image compression and for creating mosaics [ 23 ] or in the image segmentation of various geospatial images [ 24 ]. William et al [ 25 ] studied a method called V-synth to sample imbalanced data sets using Voronoi diagrams, with the authors proposing in future work to improve their results by determining accurate boundaries and synthetic points.…”

SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

“…The segmentation algorithms for dark spot detection include threshold segmentation [ 12 , 13 ], edge extraction based segmentation, wavelets [ 14 ], and fuzzy clustering [ 15 ]. SAR images are highly speckled due to coherent processing [ 16 ]. The analysis of SAR images is usually required for region and statistics-based methodology in order to reduce the speckle effect.…”