Model-based orientation-independent 3-D machine vision techniques

Abstract

Orientation-dependent techniques for the identification of a three-dimensional object by a machine vision system are represented in parts. In the first part, the data consist of intensity images of polyhedral objects obtained by a single camera, while in the second part, the data consist of range images of curved objects obtained by a laser scanner. In both cases, the attributed graphic representation of the object surface is used to drive the respective algorithm. In this representation, a graph node represents a surface patch and a link represents the adjacency between two patches. The attributes assigned to nodes are moment invariants of the corresponding face for polyhedral objects. For range images, the Gaussian curvature is used as a segmentation criterion for providing symbolic shape attributes. Identification is achieved by an efficient graph-matching algorithm used to match the graph obtained from the data to a subgraph of one of the model graphs stored in the commputer memory.