By Topic

Curve fitting by Spherical Least Squares on two-dimensional sphere

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
$31 $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

2 Author(s)
Fujiki, J. ; Nat. Inst. of Adv. Ind. Sci. & Technol., Tsukuba, Japan ; Akaho, S.

To measure the similarity between two high dimensional vector data, correlation coefficient is often used instead of Euclidean distance. For this purpose, the high dimensional vectors are mapped into hyperspherical points by normalization, and the distance between two hyperspherical data is measured as the length along geodesic on the hypersphere. Then estimations from high dimensional vector data should be resolved as minimizing appropriate energy function of the length along geodesic when high dimensional vector data are regarded as hyperspherical data. In this paper, for the first step of hyper surface fitting to hyperspehrical data, the method of curve fitting to two-dimensional spherical data by Spherical Least Squares is proposed. It is also shown that the proposed method is closely related to the curve fitting by Euclidenization of the metric.

Published in:

Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on

Date of Conference:

Sept. 27 2009-Oct. 4 2009