A New Variational Model for Joint Image Reconstruction and
Contributions The main contribution is a new variational model for joint image recon-struction and motion estimation in spatiotemporal imaging based on the LDDMM frame-work, which is studied along a general framework of the variational model that we present with deformable templates from shape theory This model contains two components: one
Joint Priors for Variational Shape and Appearance Modeling
The model we propose describes changes in motion, ap-pearance, depth ordering, and shape of a number depth lay-ers In addition, we have to learn a number of bases for the appearance space, motion space, and shape space This model is very powerful, but the notation tends to get heavy when all factors are taken into account, and the computa-
Variational method for joint optical flow estimation and edge
Wulff and Black [38] applied a layered model to jointly estimate motion and deblurring layers The most relevant work to tackle this problem is Nir et al [30], which jointly computes optical flow and denoises the image sequence within a unified variational framework Two main contributions are made in their work First, an additional
A variational method for joint tracking of curve and motion
A variational method for joint tracking of curve and motion 3 1 Introduction 1 1 Motivation and scope Tracking the contours and the motion of an object is an essential task in many applications of
Variational Mixture Smoothing for Non-Linear Dynamical Systems
variational adjustment of the resulting joint mixture approximation Our results demonstrate the effectiveness of the method on the prob-lem of inferring multiple plausible 3D human motion trajectories from monocular video Keywords: non-linear non-Gaussian systems, variational approxi-mation, mixture models, high-dimensional search
A Variational Framework for Structure from Motion in
work used in this paper for motion and depth estima-tion and the corresponding discrete operators in graph-based representations The variational depth estimation problem is presented in Section 4, and the ego-motion estimation is discussed in Section 5 Section 6 presents the joint depth and ego-motion estimation algorithm,
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