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Author (down) Mikhail Mozerov; V. Kober edit  openurl
  Title Impulse Noise Removal with Gradient Adaptive Neighborhoods Type Journal
  Year 2006 Publication Optical Engineering, 45: 67003 Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MoK2006 Serial 676  
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Author (down) Mikhail Mozerov; Joost Van de Weijer edit  doi
openurl 
  Title Accurate stereo matching by two step global optimization Type Journal Article
  Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 24 Issue 3 Pages 1153-1163  
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  Abstract 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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  ISSN 1057-7149 ISBN Medium  
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  Notes ISE; LAMP; 600.079; 600.078 Approved no  
  Call Number Admin @ si @ MoW2015a Serial 2568  
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Author (down) Mikhail Mozerov; Joost Van de Weijer edit  doi
openurl 
  Title Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering Type Journal Article
  Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 24 Issue 12 Pages 5842-5853  
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  Abstract 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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  ISSN 1057-7149 ISBN Medium  
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  Notes LAMP; 600.079;ISE Approved no  
  Call Number Admin @ si @ MoW2015b Serial 2689  
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Author (down) Mikhail Mozerov; Joost Van de Weijer edit   pdf
doi  openurl
  Title Improved Recursive Geodesic Distance Computation for Edge Preserving Filter Type Journal Article
  Year 2017 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 26 Issue 8 Pages 3696 - 3706  
  Keywords Geodesic distance filter; color image filtering; image enhancement  
  Abstract 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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  Notes LAMP; ISE; 600.120; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ Moz2017 Serial 2921  
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Author (down) Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences Type Journal Article
  Year 2010 Publication EURASIP Journal on Advances in Signal Processing Abbreviated Journal EURASIPJ  
  Volume Issue Pages  
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  Abstract Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.
 
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  ISSN 1110-8657 ISBN Medium  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MRR2010 Serial 1208  
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