Literature DB >> 15971774

Fast incorporation of optical flow into active polygons.

Gozde Unal1, Hamid Krim, Anthony Yezzi.   

Abstract

In this paper, we first reconsider, in a different light, the addition of a prediction step to active contour-based visual tracking using an optical flow and clarify the local computation of the latter along the boundaries of continuous active contours with appropriate regularizers. We subsequently detail our contribution of computing an optical flow-based prediction step directly from the parameters of an active polygon, and of exploiting it in object tracking. This is in contrast to an explicitly separate computation of the optical flow and its ad hoc application. It also provides an inherent regularization effect resulting from integrating measurements along polygon edges. As a result, we completely avoid the need of adding ad hoc regularizing terms to the optical flow computations, and the inevitably arbitrary associated weighting parameters. This direct integration of optical flow into the active polygon framework distinguishes this technique from most previous contour-based approaches, where regularization terms are theoretically, as well as practically, essential. The greater robustness and speed due to a reduced number of parameters of this technique are additional and appealing features.

Mesh:

Year:  2005        PMID: 15971774     DOI: 10.1109/tip.2005.847286

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  4 in total

1.  Image segmentation using active contours driven by the Bhattacharyya gradient flow.

Authors:  Oleg Michailovich; Yogesh Rathi; Allen Tannenbaum
Journal:  IEEE Trans Image Process       Date:  2007-11       Impact factor: 10.856

2.  Dynamic denoising of tracking sequences.

Authors:  Oleg Michailovich; Allen Tannenbaum
Journal:  IEEE Trans Image Process       Date:  2008-06       Impact factor: 10.856

3.  Segmentation of tracking sequences using dynamically updated adaptive learning.

Authors:  Oleg Michailovich; Allen Tannenbaum
Journal:  IEEE Trans Image Process       Date:  2008-12       Impact factor: 10.856

4.  Joint brain parametric T1-map segmentation and RF inhomogeneity calibration.

Authors:  Ping-Feng Chen; R Grant Steen; Anthony Yezzi; Hamid Krim
Journal:  Int J Biomed Imaging       Date:  2009-08-23
  4 in total

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