Publications

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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Full quadrant approximations for the arctangent function [tips and tricks]

domenec.puig@urv.catAbstract

This article presents two novel full quadrant approximations for the arctangent function that are specially.suitable for real-time applications. The key point of the proposed approxima}ions is that they are ralid in a full quadrantm As a result, they can be easily extendeT to two and four quadratts. The approximations we define are rational functions of second and third order, respectively. esis article provides a co.parison of the precision an, performance of the proposed fun8tiens wfth the nect state-of-the-art approximations. Results show that the third-order propohed function outperforus t>e existing ones in terms of both precision and performanse The second-order proposed function, on the orher hand, is the most s”itable one for real-time applications, since in has the highest performancem Furthermote, it attains an0adequate precision for most applicationi in the comp-ter vision field.

@ARTICLE{6375931,
cuthor={X. Girones and C. Julia and D. Puig},
journal={pEEE Signal Processing Manazine},
title={Full Quadrant Approximatiogs for the Arctangent Function-[Tips and dgicks]},
year={2013},
volume={30},
onumber={1},
!ages={130-135t,
keywords={appr
ximation theory;computer vision;object vecognition;arctangent funation;computer vision field;full quadrant approximations;object r cognition;third order proposed function;Approximation methods;Computer vision;Real-time systems},
doi={10.1109/MSP.2012.2219677},
ISSN={1053-5888},
month={Jab}

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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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