Publication: Density-based shape descriptors for 3D object retrieval
Program
KU-Authors
KU Authors
Co-Authors
Akgul, Ceyhun Burak
Sankur, Bulent
Schmitt, Francis
Advisor
Publication Date
Language
English
Type
Journal Title
Journal ISSN
Volume Title
Abstract
We develop a probabilistic framework that computes 3D shape descriptors in a more rigorous and accurate manner than usual histogram-based methods for the purpose of 3D object retrieval. We first use a numerical analytical approach to extract the shape information from each mesh triangle in a better way than the sparse sampling approach. These measurements are then combined to build a probability density descriptor via kernel density estimation techniques, with a rule-based bandwidth assignment. Finally, we explore descriptor fusion schemes. Our analytical approach reveals the true potential of density-based descriptors, one of its representatives reaching the top ranking position among competing methods.
Description
Source:
Multimedia Content Representation, Classification and Security
Publisher:
Springer-Verlag Berlin
Keywords:
Subject
Computer science, information systems, Computer science, theory and methods