research-article

An Information Fusion Framework for Robust Shape Tracking

Authors:

Xiang Sean Zhou,

Dorin Comaniciu,

Alok GuptaAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, Issue 1

Pages 115 - 129

https://doi.org/10.1109/TPAMI.2005.3

Published: 01 January 2005 Publication History

Abstract

Existing methods for incorporating subspace model constraints in shape tracking use only partial information from the measurements and model distribution. We propose a unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model. The resulting nonorthogonal subspace projection and fusion are natural extensions of the traditional model constraint using orthogonal projection. We present two motion measurement algorithms and introduce alternative solutions for measurement uncertainty estimation. We build shape models offline from training data and exploit information from the ground truth initialization online through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the motion estimation errors are heteroscedastic in nature, each heart has a distinct shape, and the relative motions of epicardial and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the existing shape-space-constrained tracking algorithm. Due to the complete treatment of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases.

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Cited By

Medley DSantiago CNascimento J(2020)Deep Active Shape Model for Robust Object FittingIEEE Transactions on Image Processing10.1109/TIP.2019.294872829(2380-2394)Online publication date: 10-Jan-2020
https://dl.acm.org/doi/10.1109/TIP.2019.2948728
Wang NGao XTao DYang HLi X(2018)Facial feature point detectionNeurocomputing10.1016/j.neucom.2017.05.013275:C(50-65)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.05.013
Nascimento JCarneiro G(2017)Deep Learning on Sparse Manifolds for Faster Object SegmentationIEEE Transactions on Image Processing10.1109/TIP.2017.272558226:10(4978-4990)Online publication date: 4-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2725582
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Index Terms

An Information Fusion Framework for Robust Shape Tracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing
    2. Image manipulation
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Random projections and metric embeddings

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 27, Issue 1

January 2005

160 pages

ISSN:0162-8828

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Copyright © 2005.

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IEEE Computer Society

United States

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Published: 01 January 2005

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Cited By

Medley DSantiago CNascimento J(2020)Deep Active Shape Model for Robust Object FittingIEEE Transactions on Image Processing10.1109/TIP.2019.294872829(2380-2394)Online publication date: 10-Jan-2020
https://dl.acm.org/doi/10.1109/TIP.2019.2948728
Wang NGao XTao DYang HLi X(2018)Facial feature point detectionNeurocomputing10.1016/j.neucom.2017.05.013275:C(50-65)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.05.013
Nascimento JCarneiro G(2017)Deep Learning on Sparse Manifolds for Faster Object SegmentationIEEE Transactions on Image Processing10.1109/TIP.2017.272558226:10(4978-4990)Online publication date: 4-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2725582
Santiago CNascimento JMarques J(2017)A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumesNeural Computing and Applications10.1007/s00521-016-2337-128:9(2489-2500)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s00521-016-2337-1
Ait Abdelali HEssannouni FEssannouni LAboutajdine D(2016)An Adaptive Object Tracking Using Kalman Filter and Probability Product KernelModelling and Simulation in Engineering10.1155/2016/25923682016(4)Online publication date: 1-Mar-2016
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Liu DTan X(2016)Local subspace smoothness alignment for constrained local model fittingNeurocomputing10.1016/j.neucom.2016.07.007214:C(785-795)Online publication date: 19-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.07.007
Zhou XZhan YRaykar VHermosillo GBogoni LPeng Z(2012)Mining anatomical, physiological and pathological information from medical imagesACM SIGKDD Explorations Newsletter10.1145/2408736.240874114:1(25-34)Online publication date: 10-Dec-2012
https://dl.acm.org/doi/10.1145/2408736.2408741
Gong HZhu S(2012)IntrackabilityInternational Journal of Computer Vision10.1007/s11263-011-0486-397:3(255-275)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1007/s11263-011-0486-3
Saragih JLucey SCohn J(2011)Deformable Model Fitting by Regularized Landmark Mean-ShiftInternational Journal of Computer Vision10.1007/s11263-010-0380-491:2(200-215)Online publication date: 1-Jan-2011
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Zhang KKwok J(2010)Simplifying mixture models through function approximationIEEE Transactions on Neural Networks10.1109/TNN.2010.204083521:4(644-658)Online publication date: 1-Apr-2010
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