New computational complexity analysis for a spatial segmentation algorithm

The emergence and increasing importance of digital society, cyber-physical systems, and semantic, pervasive, and mobile computing are expanding the role of software and applications in smart environments. Associated with these paradigms, are instruments, sensors, and 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 the 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. The 3D applications for such approaches are limited because they often fail to create meaningful partitions, due to either the complexity of the scene, or difficult lighting conditions. The paper introduces a new algorithm for spatial segmentation based on Virtual Tree-Hexagonal Structure constructed on the image´s voxels. The paper also depicts a Spatial Segmentation Algorithm. It describes the Computational Complexity Analysis of the presented Color-Based Spatial Segmentation Algorithm.