Authors:
Christian Feinen
1
;
David Barnowsky
2
;
Dietrich Paulus
2
and
Marcin Grzegorzek
1
Affiliations:
1
University of Siegen, Germany
;
2
University of Koblenz-Landau, Germany
Keyword(s):
3-D Skeleton Extraction, 3-D Curve Skeletons, 3-D Object Retrieval, 3-D Acquisition and Processing, Human Perception and Cognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Feature Selection and Extraction
;
Instance-Based Learning
;
Knowledge Acquisition and Representation
;
Object Recognition
;
Pattern Recognition
;
Perception
;
Shape Representation
;
Similarity and Distance Learning
;
Software Engineering
;
Theory and Methods
Abstract:
In this paper we introduce our approach for similarity measure of 3-D objects based on an existing curve skeletonization technique. This skeletonization algorithm for 3-D objects delivers skeletons thicker than 1 voxel. This makes an efficient distance or similarity measure impossible. To overcome this drawback, we use a significantly extended skeletonization algorithm (by Reniers) and a modified Dijkstra approach. In addition to that, we propose features that are directly extracted from the resulting skeletal structures. To evaluate our system, we created a ground truth of 3-D objects and their similarities estimated by humans. The automatic similarity results achieved by our system were evaluated against this ground truth in terms of precision and recall in an object retrieval setup.