| 1. |
- Strandmark, Petter, et al.
(författare)
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Joint Random Sample Consensus and Multiple Motion Models for Robust Video Tracking
- 2009
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Ingår i: Lecture Notes in Computer Science. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. ; 5575/2009, s. 450-459:5575, s. 450-459
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for tracking multiple objects in video captured by a non-stationary camera. For low quality video, RANSAC estimation fails when the number of good matches shrinks below the minimum required to estimate the motion model. This paper extends RANSAC in the following ways: (a) Allowing multiple models of different complexity to be chosen at random; (b) Introducing a conditional probability to measure the suitability of each transformation candidate, given the object locations in previous frames; (c) Determining the best suitable transformation by the number of consensus points, the probability and the model complexity. Our experimental results have shown that the proposed estimation method better handles video of low quality and that it is able to track deformable objects with pose changes, occlusions, motion blur and overlap. We also show that using multiple models of increasing complexity is more effective than just using RANSAC with the complex model only.
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| 2. |
- Strandmark, Petter, et al.
(författare)
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Optimal Levels for the Two-phase, Piecewise Constant Mumford-Shah Functional
- 2009
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Recent results have shown that denoising an image with the Rudin, Osher and Fatemi (ROF) total variation model can be accomplished by solving a series of binary optimization problems. We observe that this fact can be used in the other direction. The procedure is applied to the two-phase, piecewise constant Mumford-Shah functional, where an image is approximated with a function taking only two values. When the difference between the two levels is kept constant, a global optimum can be found efficiently. This allows us to solve the full problem with branch and bound in only one dimension.
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| 3. |
- Strandmark, Petter, et al.
(författare)
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Optimizing Parametric Total Variation Models
- 2009
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Ingår i: [Host publication title missing]. ; , s. 2240-2247
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Konferensbidrag (refereegranskat)abstract
- One of the key factors for the success of recent energy minimization methods is that they seek to compute global solutions. Even for non-convex energy functionals, optimization methods such as graph cuts have proven to produce high-quality solutions by iterative minimization based on large neighborhoods, making them less vulnerable to local minima. Our approach takes this a step further by enlarging the search neighborhood with one dimension. In this paper we consider binary total variation problems that depend on an additional set of parameters. Examples include: (i) the Chan-Vese model that we solve globally (ii) ratio and constrained minimization which can be formulated as parametric problems, and (iii) variants of the Mumford-Shah functional. Our approach is based on a recent theorem of Chambolle which states that solving a one-parameter family of binary problems amounts to solving a single convex variational problem. We prove a generalization of this result and show how it can be applied to parametric optimization.
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