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Author |
Mikhail Mozerov; V. Kober; I.A. Ovseyevich |
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Title |
Robust Dynamic Programming Algorithm for Motion Detection and Estimation |
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2007 |
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ISE |
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ISE @ ise @ MKO2007 |
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810 |
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Author |
Mikhail Mozerov; V. Kober |
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Title |
Impulse Noise Removal with Gradient Adaptive Neighborhoods |
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2006 |
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Optical Engineering, 45: 67003 |
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ISE |
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ISE @ ise @ MoK2006 |
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676 |
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Author |
Mikhail Mozerov; Joost Van de Weijer |
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Title |
Accurate stereo matching by two step global optimization |
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Year |
2015 |
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IEEE Transactions on Image Processing |
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TIP |
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24 |
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3 |
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1153-1163 |
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In stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques. Due to their global extend energy minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost filtering approaches obtain better results. In this paper we intend to combine both approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost fil tering methods. Based on this observation we propose to perform stereo matching as a two-step energy minimization algorithm. We consider two MRF models: a fully connected model defined on the complete set of pixels in an image and a conventional locally connected model. We solve the energy minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo datasets show that the proposed method achieves state-of-the-arts results. |
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1057-7149 |
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ISE; LAMP; 600.079; 600.078 |
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Admin @ si @ MoW2015a |
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2568 |
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Author |
Mikhail Mozerov; Joost Van de Weijer |
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Title |
Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering |
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2015 |
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IEEE Transactions on Image Processing |
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TIP |
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24 |
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12 |
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5842-5853 |
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The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. |
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1057-7149 |
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LAMP; 600.079;ISE |
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Admin @ si @ MoW2015b |
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2689 |
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Author |
Mikhail Mozerov; Joost Van de Weijer |
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Title |
Improved Recursive Geodesic Distance Computation for Edge Preserving Filter |
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Journal Article |
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2017 |
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IEEE Transactions on Image Processing |
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TIP |
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26 |
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8 |
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3696 - 3706 |
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Geodesic distance filter; color image filtering; image enhancement |
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All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application,
we consider a geodesic distance-based filter for image denoising.
Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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LAMP; ISE; 600.120; 600.098; 600.119 |
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Call Number |
Admin @ si @ Moz2017 |
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2921 |
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