Graph-Based Representations in Pattern Recognition

Abstract

A wide range of cheap and simple to use 3D scanning devices has recently been introduced in the market. These tools are no longer addressed to research labs and highly skilled professionals. By converse, they are mostly designed to allow inexperienced users to easily and independently acquire surfaces and whole objects. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. Specifically, in this paper we concentrate on the segmentation task applied to the acquired surfaces. Such a problem is well known to be ill-defined both for 2D images and 3D objects. In fact, even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reasons, we refrain from any attempt to offer an automatic solution. Instead we introduce a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by iteratively visiting a weighted graph representation of the surface starting from the supplied seeds. The assignment of each element is driven by a greedy approach that accounts for the curvature between adjacent triangles. The proposed technique does not require to perform edge detection or to fit parametrized surfaces and its implementation is very straightforward. Still, despite its simplicity, tests made on scanned 3D objects show its effectiveness and easiness of use.