By Topic

Motion Flow Estimation from Image Sequences with Applications to Biological Growth and Motility

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)
Gang Dong ; Department of Biology, University of Massachusetts, Amherst, Massachusetts 01003 USA ; Tobias I. Baskin ; Kannappan Palaniappan

In this paper, a new method for motion flow estimation that considers errors in all the derivative measurements is presented. Based on the total least squares (TLS) model, we accurately estimate the motion flow in the general noise case by combining noise model (in form of covariance matrix) with a parametric motion model. The proposed algorithm is tested on two different types of biological motion, a growing plant root and a gastrulating embryo, with sequences obtained microscopically. The local, instantaneous velocity field estimated by the algorithm reveals the behavior of the underlying cellular elements.

Published in:

2006 International Conference on Image Processing

Date of Conference:

8-11 Oct. 2006