Literature DB >> 19933014

Deform PF-MT: particle filter with mode tracker for tracking nonaffine contour deformations.

Namrata Vaswani1, Yogesh Rathi, Anthony Yezzi, Allen Tannenbaum.   

Abstract

We propose algorithms for tracking the boundary contour of a deforming object from an image sequence, when the nonaffine (local) deformation over consecutive frames is large and there is overlapping clutter, occlusions, low contrast, or outlier imagery. When the object is arbitrarily deforming, each, or at least most, contour points can move independently. Contour deformation then forms an infinite (in practice, very large), dimensional space. Direct application of particle filters (PF) for large dimensional problems is impractically expensive. However, in most real problems, at any given time, most of the contour deformation occurs in a small number of dimensions ("effective basis space") while the residual deformation in the rest of the state space ("residual space") is small. This property enables us to apply the particle filtering with mode tracking (PF-MT) idea that was proposed for such large dimensional problems in recent work. Since most contour deformation is low spatial frequency, we propose to use the space of deformation at a subsampled set of locations as the effective basis space. The resulting algorithm is called deform PF-MT. It requires significant modifications compared to the original PF-MT because the space of contours is a non-Euclidean infinite dimensional space.

Entities:  

Year:  2009        PMID: 19933014      PMCID: PMC3683548          DOI: 10.1109/TIP.2009.2037465

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


  5 in total

1.  Contour-based object tracking with occlusion handling in video acquired using mobile cameras.

Authors:  Alper Yilmaz; Xin Li; Mubarak Shah
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2004-11       Impact factor: 6.226

2.  Dynamical statistical shape priors for level set-based tracking.

Authors:  Daniel Cremers
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2006-08       Impact factor: 6.226

3.  Segmenting and tracking the left ventricle by learning the dynamics in cardiac images.

Authors:  W Sun; M Qetin; R Chan; V Reddy; G Holmvang; V Chandar; A Willsky
Journal:  Inf Process Med Imaging       Date:  2005

4.  Active contours without edges.

Authors:  T F Chan; L A Vese
Journal:  IEEE Trans Image Process       Date:  2001       Impact factor: 10.856

5.  Tracking deforming objects using particle filtering for geometric active contours.

Authors:  Yogesh Rathi; Namrata Vaswani; Allen Tannenbaum; Anthony Yezzi
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2007-08       Impact factor: 6.226

  5 in total
  1 in total

1.  Tracking monotonically advancing boundaries in image sequences using graph cuts and recursive kernel shape priors.

Authors:  Joshua C Chang; K C Brennan; Tom Chou
Journal:  IEEE Trans Med Imaging       Date:  2011-12-05       Impact factor: 10.048

  1 in total

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