Efficient and reliable template set matching for 3D object recognition

Authors:
Michael Greenspan;Pierre Boulanger
Affiliations:
Deptartment of Systems and Computer Engineering, Carleton University, Ottawa, Canada and Institute for Information Technology, National Research Council Canada;Institute for Information Technology, National Research Council Canada
Venue:
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Year:
1999

Citing 6
Cited 2

Shape from probing

Journal of Algorithms
Determination of Three-Dimensional Object Location and Orientation from Range Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Decision Trees and Diagrams

ACM Computing Surveys (CSUR)
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Multilevel Approach to Sequential Detection of Pictorial Features

IEEE Transactions on Computers

Geometric Probing of Dense Range Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of local and global geometrical cues for 3D face recognition

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Object recognition in range image data is formulated as template set matching. The object model is represented as a set of voxel templates, one for each possible pose. The set of all templates is composed into a binary decision tree. Each leaf node references a small number of templates. Each internal node references a single voxel, and has two branches, T and F. The subtree branching from the T branch contains the subset of templates which contain the node voxel, Conversely, the subtree branching from F branch contains the subset of templates which do not contain the node voxel. Traversing the tree at any image location executes a point probe strategy. It efficiently determines a good match with the template set by interrogating only those elements which discriminate between the remaining possible interpretations. The method has been implemented for a number of different heuristic tree design and traversal methods, Results are presented of extensive tests for two objects under isolated, cluttered, and occluded scene conditions. It is shown that there exist traversal/design combinations which are both efficient and reliable, and that the method is robust.