By Topic

Surface matching by curvature distribution images generated via gaze modeling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Makoto Maeda ; Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan ; Takashi Nakamae ; Katsuhiro Inoue

In order to realize model-based 3D object recognition, first, we propose a geometric feature extraction method based on a novel gaze modeling. In the modeling process, local surface models are independently estimated for parts of range data restricted by several gaze domains. Hence, since features are independently extracted from each gaze domain, inconsistent or incorrect features may be obtained. Therefore we introduce a stochastic method that enables us to integrate such features by evaluating the reliability of each gaze model. Next, we propose a shape descriptor, curvature distribution image (CDI), to achieve object recognition by surface matching. It is generated based on the ratios between surface curvatures. The main contribution of this paper is experimental analysis of the performance of CDIs generated by various generation parameters.

Published in:

Pattern Recognition (ICPR), 2012 21st International Conference on

Date of Conference:

11-15 Nov. 2012