Efficient Graph-Based Volumetric Segmentation

Abstract

The emergence and increasing importance of digital society increased the role of software applications in smart environments. Associated with these paradigms are a multitude of applications that generate and require analysis of massive volumes of diverse, heterogeneous, complex, and distributed data. The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. There is a wide range of computational vision problems for 2D images that could use of segmented images. However the problems of 3D image segmentation and grouping remain great challenges for computer vision. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. Many approaches aim to create large regions using simple homogeneity criteria based only on color or texture. However, 3D applications for such approaches are limited as they often fail to create meaningful partitions due to the computation complexity. We are introducing new algorithm for spatial segmentation based on Virtual Tree-Hexagonal Structure constructed on the image voxels. Then the paper depicts a Spatial Segmentation Algorithm. Spatial Segmentation Algorithm contains many other algorithms but only Color-based segmentation algorithm is presented based on the limited space of paper. Then the paper describes the Computational Complexity Analysis of the Color-Based Spatial Segmentation Algorithm.