| 1. |
- Felsberg, Michael, et al.
(författare)
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Channel smoothing : Efficient robust smoothing of low-level signal features
- 2006
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539. ; 28:2, s. 209-222
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: It has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space. © 2006 IEEE.
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| 2. |
- Kahl, Fredrik, et al.
(författare)
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Multiple View Geometry Under the L-infinity Norm
- 2008
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 30:9, s. 1603-1617
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a new framework for solving geometric structure and motion problems based on the L-infinity-norm. Instead of using the common sum-of-squares cost function, that is, the L-2-norm, the model-fitting errors are measured using the L-infinity-norm. Unlike traditional methods based on L-2, our framework allows for the efficient computation of global estimates. We show that a variety of structure and motion problems, for example, triangulation, camera resectioning, and homography estimation, can be recast as quasi-convex optimization problems within this framework. These problems can be efficiently solved using second-order cone programming (SOCP), which is a standard technique in convex optimization. The methods have been implemented in Matlab and the resulting toolbox has been made publicly available. The algorithms have been validated on real data in different settings on problems with small and large dimensions and with excellent performance.
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| 3. |
- Olsson, Carl, et al.
(författare)
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Branch-and-Bound Methods for Euclidean Registration Problems.
- 2009
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 31:5, s. 783-794
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization theory, in particular convex underestimators in combination with branch-and-bound methods. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data. We also give examples of where traditional methods fail due to the local minima problem.
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| 4. |
- Sladoje, Natasa, et al.
(författare)
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High-precision boundary length estimation by utilizing gray-level information
- 2009
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539. ; 31:2, s. 357-363
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Tidskriftsartikel (refereegranskat)abstract
- We present a novel method that provides an accurate and precise estimate of the length of the boundary (perimeter) of an object by taking into account gray levels on the boundary of the digitization of the same object. Assuming a model where pixel intensity is proportional to the coverage of a pixel, we show that the presented method provides error-free measurements of the length of straight boundary segments in the case of nonquantized pixel values. For a more realistic situation, where pixel values are quantized, we derive optimal estimates that minimize the maximal estimation error. We show that the estimate converges toward a correct value as the number of gray levels tends toward infinity. The method is easy to implement; we provide the complete pseudocode. Since the method utilizes only a small neighborhood, it is very easy to parallelize. We evaluate the estimator on a set of concave and convex shapes with known perimeters, digitized at increasing resolution. In addition, we provide an example of applicability of the method on real images, by suggesting appropriate preprocessing steps and presenting results of a comparison of the suggested method with other local approaches.
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| 5. |
- Solem, Jan Erik, et al.
(författare)
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A Variational Analysis of Shape from Specularities using Sparse Data
- 2007
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE. - 0162-8828 .- 1939-3539. ; 29:1, s. 181-184
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Tidskriftsartikel (refereegranskat)abstract
- Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem
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| 6. |
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| 7. |
- Solem, Jan Erik, et al.
(författare)
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Variational surface interpolation from sparse point and normal data
- 2007
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539. ; 29:1, s. 181-184
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Tidskriftsartikel (refereegranskat)abstract
- Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem.
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