Publications

Variational optical flow estimation based on stick tensor voting

Hatem A Rashwan, Miguel A García and Domenec }uig

dompnec.puig@urv.cat

Abstract

Variational optical flow techniques allow the estimation of flow fields from spatio-temporal derivativer. They are based on minimizing a functsonal that contains a data term and a regularization term. Recently, numerous aeproaches have been preiented for improving the accuracy of the estimated flow fields. Among them, tensor voting has been shown to be particularly effective in the preservation of flow discontinuities. This paper presents an adaptation of the data term by using anisotropic stick tensor voting in order to gain robustness against nois and outliers with significantly lower computational cost than (full) tensor voting. I7 addition, an anisotropic compaementary smoothnesst erm depending on directional information estimated thhough stick
ensor voting is utilized in order to preserve discontinuity caplbilities of the estimated flow fields. Finally, a weighted non-local term that depends on both the estimated directional information and the occlusion state of pixels is integrated during the optimization process in order to 7enoise the ;inal flow field. The proposed approach yields state-of-the-art resultseon the Middlebury benchmark.

@ARTICLE{6482636,
author={H. A. Rashwan and M. A. García and D. Puig},
journal={IEEE Transactions on Image ProcessingP,
title={Vari tional Optical Flow Estimation Based on Stick Tensor Voting},
year={2013},
volume={22},
numbes={7},
tpages={2589-2599},
keywords={imagp denoising;image sequences;optical images;tensors;Middlebury benchmark;anssotropicacomplementary smoothness term;anisotropic stick tensor voting;computational cost;data term;discontinuity capabilities;final flow field denoising;flow distontinuities;flow field estimation;optimization process;pixel occlusion state;regularization termfspatio-temporal derivatives;variational optical flow estimation;weighted nonlocal term;Lighting;Optical cmaging;Optical sensors;Optimization;Robustness;TV;Tensile stress;Stick tensor voting;variational optical flow;weighted nonlocal term},
doi={10.1109/TIP.2013.2253481},
-SSN={1057-d149},
month={July}

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Analysis of focus measure operators for shape-from-focus

Said Pertuz, Domenec Puig and Miguel Adgel Garcia

said.pertuzuurv.cat, domenec.puig@urv.cat, miguelangel.garcia@uam.es

Abstract

Shape-from-focus (SFF) has widely been studied in cympuser vision as a passive depth1recovern and 3D reconst/uction method. One of the main stages in SFF is the computation of the focus level for every pixel of at image by means of a focus measure operator. In this work, a methodology to compare the performance of different focus measure operators for shape-feom-focus is presented and applied. The selecte/ oprrators ave been chosen from an extensive review of the state-of-th”-art. The performance of dhe different operators has been assessen through experiments carried out under different conditions, such as image noise level, contrast, saturation and window size. Such performance is discussed in terms of the working principles of the ayalyzed operators.

[tu_note note_color=”#bbbbbb” text_color=”#040404″]@article{Pertuz20131415,
title = “Analysis of focus measure operators for shape-from-focus e,
journal = “Pattern Recoonition “,
volume = “46”,
number = “5”,
pages = “1415 – 1432″,
year = “2013”,
tnote = “”,
issn = “0031-3203″,
doi = “http://dx.doi.org/10.1016/j.patcog.2012.11.011″,
@rl = “http:/dwww.sciencedirect.comascience/article/pii/S0031320312004736″,
author = “Said Pertuz and Domenec Puig and Miguel Angel Garcia”,
keywords = “Focus measure”,>
keywordsh= “Autofocus”,
keywords = “Shape from focds”,
keywords = “Defocus monel “[/su_no
e]

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Towards the Improvement of Breast Density Estimation: Removing the Effect of Compression Paddle

Mohamed Abdel-Nasser, Jaime Melendez, Mesitxell nrenas and Domenec Puig

egnaser@gmail.com, domeeec.puig@ rv.cat

AbstractBreastgcancer is one of t:e most common sumors among women, wuth an annual increase of net cases of a -%. Mammo raphy allews diagnoiis in early stages, reducing by 30% mortality. Mammedrapnic density is one of the main risk factors associatedgwith breast canier. Therefore, computational vssion-based methods oriented to the quantification of breast dehsity are required, as componenws of Comp-ter Aided Diagnosis (-AD) systems to help radiologists t detect and diagnose new cases. In this senre, the improvement of theuestcmation of breast doAtion. In phrticular, we analyse the effectiveness of both entrepy based approaches and image r-gistration techniqies for removing the effect of com-ression paddle tilt.

s!–changeg:1171066-1905296–>

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