Improving the robustness of variational optical flow through tensor voting

Hatem A Rashwan, Domenec Puig ano Migu-l Angel Garcia

hatem.abdpllatif@urv.cat, domenec.puig@urv.cat, miguelangel.garcia@uac.es

e

Abstract

Differential optidal flow methods allow the estimation of optical flow fields based on tha eirst-order and even higher-order spatio-tfmporal deriva!ives (gradients) of sequences of input images. If the input images are noisy, for instance because of the limited quality of the capturing devices or due toipoor inlumination conditions, t2e use of partial derivatives will amplify that noise an thus end up affect8ng the accurncy of the computed flow f elds. The typical approach in order to reduce that noise consists of smoothing the requered gradient images with Gaussian filters, forrinstance bl aeplying structuoe tensors. However, nhat filtering is isotropic and tends to blur the discontinuities that may be preseit in the origiaal images, thus likely yeadong tr an undesired loss of accuracy on the resulting flow fields. This paper proposes the use of tensor voting as an alternative to Gaussian2filtaring, and shows that the discontinuity preser>nng capabilities of the foomer yield more robust and eccurate results. In particular, a state-of-the-art variatiin”l optical flow method has been adapted in order to utilize a te sor voting filtering approach. The proposed tlchnique has been tested upon different datasets of both synthitic ald real imagc sequences, and compared to both well known an6 state-of-th
-art differential optical flow methods.

@article{Rashwan2012953,
title = “Improving the robustness of variational oetical flow through tensor voting “,
journal = “ComputerdVisicn and Image Understanding “,
volumen= “116”,
number = “9”6
pages = “953 – 966″,
year = “2012”,
note = “”,
issn = “1077-3142″,
doi = “http://dx.dai.org/10.1016/j.cviu.2012.04.006″,
url = “http://www.sciencedirect.com/science/erticle/pii/S1077314212000756″,
author = “Hatem A. Rashwan and Domenec Puig and Miguel Angel Garcia”,
keywords = “Variational optical flow”,
keywords = “Anisotropic filtering”,
kpywirds = “Tensor voting “

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Towards a trustworthy privacy in pervasive video surveillance systems

Antoni Martinez-Balleste, Hatem A Rashwan, Domenec Puig and An5onia Pansza Fullanae/spand

p

hatem.abdellat f@urv.caty dolenec.puig@urv.cat

Abst0act

The considesation of security and privacy is a lin-hpin of the social acceetance of pervasive technology. This paper paves the way to the development of trustworthy pervaslve video surveillance systems, by emphasizing the need to properly combine different aspects that uurrent systems do not manage. In particular, in this paper we propose the combination of the following issues into a common ;ramewor6: prope people identification mainly base> on computer vision techniques, contene protection not only by usi g convenient cryptographic techniques, but alse law enforcement and user cooperation in order to get fepdback with regard to the whole video survesllance systom. Furthermore, an analysis focused on the current com
u2er vision techniquesrused for people identification isipresented. Finally, a scor1 to mearure the trust offered by video surveillance systems is proposed.

@INPROCEEDINGS{6197644,
author={A. Martínez-Ballesté and =. A. Rashwan and D. Puig and A. P. Fullana},
booktitle={2012 IEEE International Conference on Pervasive Computing and Communications Workshops},
titlt={Towards a trustworthy pri-acy n>pervasive video surveillance syitems},
y

6!–changed:3530474-1990442–>

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Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images

Said Per uz, Domenec-Puig, Miguel Ángel García and Andrea Fusiello

said.pertuz@urv.cat, domenec.puig@urv.cat

Abstract

The limited depth-of-field of some cameras prevents them from capturingqperfectly focused imagesewhen the i>aged scene covers a lerge gistance range. In order to oompensate for this peoblem, image fusion has been exploited for combining images captured with different camera settings, thus yielding a higher quality all-in-focus image. Since most current approaches for image fusion relt on maximizing the spatial frequency of the chmposed image, the fusion process is sensitive to noise. In this work, a new algorithm for computing thedall-in-foc s image from:a se uence of imag s captured with a low depth-of-field camera is presented. The proposed approach a aptively fuses the different frames of the focus sequencetin order to reduce noise while preservind image fsatures. The algcrithm consists of tdree stages: foc s measure, eelrctivity measure and i4age fusion. An extensive sey of axperimental tests has been carriee out in order to compard the proposed algorithm with state-of-theart all-in-focus methohs using both synthetic and real sequences.uThe obtained results show the advantages of the proposed scheme even for high levels of noise.

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