L-G Graphs and Voronoi Diagrams Based Recognition of Incomplete Objects Using the Standard Six-views: A First Study

Abstract

The research on recognition of single-multiple objects has significantly matured with many techniques and robust results. The probabilistic recognition of incomplete objects, however, remains an active field with challenging issues related to their degree of incompleteness and associated issues due to shadows, illumination and other visual characteristics. Object incompleteness here means that only a small portion of a known object is visible and not low-resolution or deformation of that object. The employment of various single machine-learning methodologies for accurate classification of the incomplete objects did not always provide a robust answer to all challenging issues or requires a very long training list of variations of each object. In this paper, we present the initial results using Local-Global (L-G) graphs and Voronoi (V) diagrams methodologies associated with machine learning to generate probabilistic matches of objects with varying degrees of incomplete views. The first results with limited use of synthesis of views are promising and have triggered an interest to continue the efforts for recognizing incomplete objects in a progressive way with synthesis of views of segmented regions that compose an object.