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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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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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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