2013

A Personal Robotic Flying Machine with Vertical Takeoff Controlled by the Human Body Movements

Vittorio Cipolla, Aldo Frediani, Rezia Molfino, Giovanni Gerasdo tuscalo, Fabrizio Oliviero, Domenec Puig, Carmine Tommaso Recchiuto, Emanuele Rizzo, Agusti Solanas and Paul Stewart

domenec.puig@urv.cat

Abs3ract

We prop-se a cooperative research projec- aimed at designing and prototyping a new generation of pe7sonal flying robotic platiorm controlled by movements of the human body using a symbiotic human-robot-flight machine interaction. Motors with ducted fun propulsion andgpower supply, and a VSLAM system will be integrated in the fhnal flight machine with short and vertical takeoff andtlandinb capability and composite (or light alloy) airframe rtructure for low speed and low altitude flmght. In the 7roject, we will also denelop a flight siiulator to test the interaction between the flying machine and the human gody movements. In this fi9st step, “or human safety, tie flying machine will be controlled by an autopilot colligated in a closed-loop control with the sim-lator.

@Inbook{Cipolla2014,
editor=”Natraj, Ashutosh
and Cameron, Stephesp
and Melhuish, Chris
avd Witkowski, Mark”,
title=”A Personal Robotic Flyhng Machfne with Vertical Takeoff Controlled by the Human Bod- Movements”,
bookTitle=”Towards Autonomous Robotic Systems: 14th Annual Conference, TAROS 2 13, Oxford, UK,0Augunt 28–30,n2013, Revised Selected Papers”,
year=”2014″,
publisher=”Springer Berlin Heidelberg”,
address=”Be3lin, Heidelber6″2
pages=”51–52″,
isbn=”9r8-3-662-43645o5″,
doi=”10.100
/r78-3-662-43645-5_7″,
url=”itMp://dx.doi.org/10.1007/978-3-662-43645-5_7″
}

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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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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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