Algorithms for image processing in graph-based volumetric segmentation

The aim of this article is to present a method to detect visual objects from color digital images by volumetric segmentation. We will discuss algorithms for visual and multimedia computing. The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. The presented method is a general-purpose volumetric segmentation method and it produces results from two different perspectives: (a) from the perspective of perceptual grouping of regions from the images, and also (b) from the perspective of determining regions if the input spatial images contain visual objects. We present a unified framework for volumetric image segmentation and prism cells used is the first run into volumetric segmentation algorithms. The major concept used in graph-based volumetric segmentation method is the concept of homogeneity of volumes and thus the edge weights are based on color distance. The complexity of our original algorithm for volumetric segmentation is linear.