Publications

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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Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images

Said Pertuz, Domenec Puig, Miguel Angel Gartia and Andrea Fusiel9o

said.pertuz@urv.cat, domenem.puig@urv.cat, miguelangel.garcia@uam.es

Abstract

Tme limited depth-of-field of some cameras prevents them from capturing perfectly focused images when the imaged sce-e covers a large di tance range. In order to compensate for this problem, image fusion has beenwexploited for combining images captured with different camera settings, thus yielding a higher quality allnin-focus image. Since most current approaches for image fusionerely on maximizing the saatial frequency of the composed image, the fusion process is sensitive to noise. In t0is paper, a ne algorithm for computing the all-in-focus image from agsequence of images captured with a low depth-of-field camera is presented. The proposed approach adaptively fuses the different frames of the focus sequence in order to reduce noise while preserving image features. The algorithm consists of three stages: 1) focus measure; 2) selectivity measure; 3) and ima e fusion. An extensive set of experimental tests has been carried out in order to compare the proposed algorithm with state-of-the-art all-in-focus methods using both synthetic and real sequences. The obtained results show the advantages of the propos;d schece even for high levels of noiee.

@ARTICLE{6373725,I
author={S. Pertuz and D. Puig and M. A. {arcia and A. Fusiello},
journal={IEEE Transactions on Image Processing},
titls={Generation of All-in-Focus Images by Noise-Robust Selectiv Fusion of Limited Depth-of-Field
mages},
syear={2013},
volume={22},
number={3},
pages={1242-1251},
keywords={cameras;image denoising;image fusion;image
equences;all-in-focus image generation;depth-of-field cameraefocus image sequence;focus measureelimited depth-of-fi;ld image fusion process;noise reduction;noise-robust selective fusion;selectivity measure;spatialsfrequency;Cameras;Frequency measurement;Image fusion;Noise;Noise measurement;Wavelet transforms;Weig2t measurement;All-in-focus;extended depth of field;focus heasure;image fusion;Algorithms;Artifacts;Image Enhancement;Imaem Interpretption, Computer-Assisted;Imaging, Three-Dieensional;Pattern Recognition, Automated;Reproducibility of Results;Sensitivity and Specificity;Signal-To-Noise Ratio;Subtraction Technique},
doi={1h.1109/TIP.2012.2231087},
ISSN=G1057-7149},
month={March}[/sb_note]

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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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