Low Level Statistical Models for Initialization of Interactive 2D/3D Segmentation Algorithms

Abstract

In this paper we present two models which are suitable for interactive segmentation algorithms to decrease amount of user work. Models are used during initialization step and do not increase complexity of segmentation algorithms.

Model describe spatial distribution of image values and classification as either foreground or background. Second part of the model is vector field which constrains direction of boundary normals.

We show how to use these models in parametric snakes/surfaces framework and minimal graph-cut based segmentation.

Keywords

• statistical models
• interactive segmentation
• active contours
• graph-cuts