452 research outputs found
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No full textIn this paper, we present an algorithm for fast calculation of the normalized cross correlation and its application to the problem of template matching. Given a template t, whose position is to be determined in an image f, the basic idea of the algorithm is to represent the template, for which the normalized cross correlation is calculated, as a sum of rectangular basis functions. Then the correlation is calculated for each basis function instead of the whole template. The result of the correlation of the template t and the image f is obtained as the weighted sum of the correlation functions of the basis functions. Depending on the approximation, the algorithm can by far outperform Fourier-transform based implementations of the normalized cross correlation algorithm and it is especially suited to problems, where many different templates are to be found in the same image f
Classification of tree species and standing dead trees by fusing UAV-based lidar data and multispectral imagery in the 3D deep neural network PointNet++
No full textKnowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice
Erratum: Search for photons with energies above 1018 eV using the hybrid detector of the Pierre Auger Observatory (Journal of Cosmology and Astroparticle Physics (2017) 4 (9) DOI: 10.1088/1475-7516/2017/04/009)
No full text1 Exposure calculation Due to a mistake in the numerical integration following eq. (6.2) of the original article [1], the exposure shown in figure 5 of the original article was incorrect. The correct exposure is shown in figure 1. 2 Upper limits on the integral photon flux and fraction The incorrect exposure affects the calculation of the upper limits on the integral photon flux following eq. (6.1) of the original article. The correct values for the upper limits are 0.038, 0.010, 0.009, 0.008 and 0.007 km−2 sr−1 yr−1 for threshold energies of 1, 2, 3, 5 and 10 EeV. The correct values for the upper limits on the integral photon fraction subsequently derived are 0.14 %, 0.17 %, 0.42 %, 0.86 % and 2.9 % for the same threshold energies. 3 Author list The author list of this erratum also corrects a mistake made in the original article, where F. Zuccarello was missing and Z. Zong was listed twice
Erratum: Search for photons with energies above 1018 eV using the hybrid detector of the Pierre Auger Observatory
No full textExposure calculation Due to a mistake in the numerical integration following eq. (6.2) of the original article [1], the exposure shown in figure 5 of the original article was incorrect. The correct exposure is shown in figure 1. 2 Upper limits on the integral photon flux and fraction The incorrect exposure affects the calculation of the upper limits on the integral photon flux following eq. (6.1) of the original article. The correct values for the upper limits are 0.038, 0.010, 0.009, 0.008 and 0.007 km−2 sr−1 yr−1 for threshold energies of 1, 2, 3, 5 and 10 EeV. The correct values for the upper limits on the integral photon fraction subsequently derived are 0.14 %, 0.17 %, 0.42 %, 0.86 % and 2.9 % for the same threshold energies. 3 Author list The author list of this erratum also corrects a mistake made in the original article, where F. Zuccarello was missing and Z. Zong was listed twice
Eigenschaften und Dimensionierung von Koaxialkabeln, Streifenleitungen, Finleitungen, Richtkopplern und Hochfrequenzfiltern
No full textClassification of tree species and standing dead trees by fusing UAV-based lidar data and multispectral imagery in the 3D deep neural network PointNet++
No full textKnowledge of tree species mapping and of dead wood in particular is fundamental to managing our forests. Although individual tree-based approaches using lidar can successfully distinguish between deciduous and coniferous trees, the classification of multiple tree species is still limited in accuracy. Moreover, the combined mapping of standing dead trees after pest infestation is becoming increasingly important. New deep learning methods outperform baseline machine learning approaches and promise a significant accuracy gain for tree mapping. In this study, we performed a classification of multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D deep neural network (DNN) PointNet++ along with UAV-based lidar data and multispectral (MS) imagery. Aside from 3D geometry, we also integrated laser echo pulse width values and MS features into the classification process. In a preprocessing step, we generated the 3D segments of single trees using a 3D detection method. Our approach achieved an overall accuracy (OA) of 90.2% and was clearly superior to a baseline method using a random forest classifier and handcrafted features (OA = 85.3%). All in all, we demonstrate that the performance of the 3D DNN is highly promising for the classification of multiple tree species and standing dead trees in practice
A 3-Year Sample of Almost 1,600 Elves Recorded Above South America by the Pierre Auger Cosmic-Ray Observatory
No full textElves are a class of transient luminous events, with a radial extent typically greater than 250 km, that occur in the lower ionosphere above strong electrical storms.We report the observation of 1,598 elves, from 2014 to 2016, recorded with unprecedented time resolution (100 ns) using the fluorescence detector (FD) of the Pierre Auger Cosmic-Ray Observatory. The Auger Observatory is located in the Mendoza province of Argentina with a viewing footprint for elve observations of 3 · 106 km2, reaching areas above the Pacific and Atlantic Oceans, as well as the Córdoba region, which is known for severe convective thunderstorms. Primarily designed for ultrahigh energy cosmic-ray observations, the Auger FD turns out to be very sensitive to the ultraviolet emission in elves. The detector features modified Schmidt optics with large apertures resulting in a field of view that spans the horizon, and year-round operation on dark nights with low moonlight background, when the local weather is favorable. The measured light profiles of 18% of the elve events have more than one peak, compatible with intracloud activity. Within the 3-year sample, 72% of the elves correlate with the far-field radiation measurements of the World Wide Lightning Location Network. The Auger Observatory plans to continue operations until at least 2025, including elve observations and analysis. To the best of our knowledge, this observatory is the only facility on Earth that measures elves with year-round operation and full horizon coverage.
Co-authors: A. Aab, P. Abreu,M. Aglietta, I. F.M. Albuquerque, J.M. Albury, I. Allekotte, A. Almela, J. Alvarez Castillo, J. Alvarez-Muniz, G. A. Anastasi,L. Anchordoqui, B. Andrada, S. Andringa, C. Aramo, H. Asorey, P. Assis, G. Avila, A.M. Badescu, A. Bakalova, A. Balaceanu,F. Barbato, R. J. Barreira Luz, S. Baur, K. H. Becker, J.A. Bellido, C. Berat,M. E. Bertaina, X. Bertou, P. L. Biermann, J. Biteau, S. G. Blaess, A. Blanco, J. Blazek, C. Bleve,M. Bohaˇcova, D. Boncioli, C. Bonifazi, N. Borodai, A. M. Botti, J. Brack,T. Bretz, A. Bridgeman, F. L. Briechle, P. Buchholz, A. Bueno, S. Buitink,M. Buscemi, K. S. Caballero-Mora, L. Caccianiga, L. Calcagni, A. Cancio, F. Canfora, J.M. Carceller, R. Caruso, A. Castellina, F. Catalani, G. Cataldi, L. Cazon,M. Cerda, J. A. Chinellato,J. Chudoba, L. Chytka, R.W. Clay, A. C. Cobos Cerutti, R. Colalillo, A. Coleman,M. R. Coluccia, R. Conceicao, A. Condorelli, G. Consolati,F. Contreras,M. J. Cooper, S. Coutu, C. E. Covault, B. Daniel, S. Dasso, K. Daumiller, B. R. Dawson, J. A. Day, R.M. de Almeida,S. J. de Jong, G. Mauro, J. R. T. de Mello Neto, I. Mitri, J. de Oliveira, F. O. de Oliveira Salles, V. de Souza, J. Debatin,M. del Rio, O. Deligny,N. Dhital,M. L. Diaz Castro, F. Diogo, C. Dobrigkeit, J. C. D\u27Olivo, Q. Dorosti, R. C. dos Anjos, M. T. Dova, A. Dundovic, J. Ebr, R. Engel,M. Erdmann, C. O. Escobar, A. Etchegoyen, H. Falcke, J. Farmer, G. Farrar, A. C. Fauth, N. Fazzini, F. Feldbusch,F. Fenu, L. P. Ferreyro, J.M. Figueira, A. Filipˇciˇc, M. M. Freire, T. Fujii, A. Fuster, B. Garcia, H. Gemmeke, A. Gherghel-Lascu,P. L. Ghia, U. Giaccari,M. Giammarchi,M. Giller, D. Głas, J. Glombitza, F. Gobbi, G. Golup,M. Gomez Berisso, P. F. Gomez Vitale,J. P. Gongora, N. Gonzalez, I. Goos, D. Gora, A. Gorgi,M. Gottowik, T. D. Grubb, F. Guarino, G. P. Guedes, E. Guido,R. Halliday, M. R. Hampel, P. Hansen, D. Harari, T. A. Harrison, V. M. Harvey, A. Haungs, T. Hebbeker, D. Heck, P. Heimann,G. C. Hill, C. Hojvat, E. M. Holt, P. Homola, J. R. Horandel, P. Horvath,M. Hrabovsky, T. Huege, J. Hulsman, A. Insolia,P. G. Isar, I. Jandt, J. A. Johnsen,M. Josebachuili, J. Jurysek, A. Kaapa, K. H. Kampert, B. Keilhauer, N. Kemmerich, J. Kemp,H. O. Klages, M. Kleifges, J. Kleinfeller, R. Krause, D. Kuempel, G. Kukec Mezek, A. Kuotb Awad, B. L. Lago, D. LaHurd, R. G. Lang,R. Legumina,M. A. Leigui de Oliveira, V. Lenok, A. Letessier-Selvon, I. Lhenry-Yvon, O. C. Lippmann, D. Lo Presti, L. Lopes, R. Lopez, A. Lopez Casado,R. Lorek, Q. Luce, A. Lucero,M. Malacari, G. Mancarella, D. Mandat, B. C. Manning, P. Mantsch, A. G. Mariazzi, I. C. Mari，s, G. Marsella, D. Martello, H. Martinez, O. Martinez Bravo,M. Mastrodicasa, H. J. Mathes, S. Mathys, J. Matthews, G. Matthiae, E. Mayotte,P. O. Mazur, G. Medina-Tanco, D. Melo, A. Menshikov, K.-D. Merenda, S. Michal,M. I. Micheletti, L. Middendorf, L. Miramonti, B. Mitrica,D. Mockler, S. Mollerach, F. Montanet, C. Morello, G. Morlino,M. Mostafa, A. L. Muller,M. A. Muller, S. Muller, R. Mussa,L. Nellen, P. H. Nguyen,M. Niculescu-Oglinzanu,M. Niechciol, D. Nitz, D. Nosek, V. Novotny, L. Noža, A Nucita, L.A. Nunez,A. Olinto,M. Palatka, J. Pallotta,M. P. Panetta, P. Papenbreer, G. Parente, A. Parra, M. Pech, F. Pedreira, J. Pe，kala,R. Pelayo, J. Pena-Rodriguez, L. A. S. Pereira,M. Perlin, L. Perrone, C. Peters, S. Petrera, J. Phuntsok, T. Pierog,M. Pimenta,V. Pirronello,M. Platino, J. Poh, B. Pont, C. Porowski, R. R. Prado, P. Privitera,M. Prouza, A. Puyleart, S. Querchfeld,S. Quinn, R. Ramos-Pollan, J. Rautenberg, D. Ravignani, M. Reininghaus, J. Ridky, F. Riehn,M. Risse, P. Ristori, V. Rizi, W. Rodrigues de Carvalho, J. Rodriguez Rojo,M. J. Roncoroni, M. Roth, E. Roulet, A. C. Rovero, P. Ruehl, S. J. Saffi, A. Saftoiu, F. Salamida,H. Salazar, G. Salina,J. D. Sanabria Gomez, F. Sanchez, E.M. Santos, E. Santos, F. Sarazin, R. Sarmento, C. Sarmiento-Cano, R. Sato,P. Savina,M. Schauer, V. Scherini, H. Schieler, M. Schimassek,M. Schimp, F. Schluter, D. Schmidt, O. Scholten, P. Schovanek, F. G. Schroder, S. Schroder, J. Schumacher, S. J. Sciutto,M. Scornavacche, R. C. Shellard, G. Sigl, G. Silli, O. Sima, R. Šmida,G.R. Snow, P. Sommers, J. F. Soriano, J. Souchard, R. Squartini, D. Stanca, S. Staniˇc, J. Stasielak, P. Stassi, M. Stolpovskiy,A. Streich, F. Suarez,M. Suarez-Duran, T. Sudholz, T. Suomijarvi, A.D. Supanitsky, J. Šupik, Z. Szadkowski, A. Taboada, O. A. Taborda, A. Tapia, C. Timmermans, C. J. Todero Peixoto, B. Tome, G. Torralba Elipe, A. Travaini, P. Travnicek,M. Trini, M. Tueros, R. Ulrich,M. Unger,M. Urban, J. F. Valdes Galicia, I. Valino, L. Valore, P. van Bodegom, A.M. van den Berg, A. van Vliet, E. Varela, B. Vargas Cardenas,D. Veberiˇc, C. Ventura, I. D. Vergara Quispe, V. Verzi, J. Vicha, L. Villasenor, J. Vink, S. Vorobiov, H. Wahlberg, A. A. Watson, M. Weber, A. Weindl,M.Wieden´ ski, L. Wiencke, H. Wilczyn´ ski, T.Winchen, M. Wirtz, D. Wittkowski, B. Wundheiler, L. Yang, A. Yushkov, E. Zas, D. Zavrtanik, M. Zavrtanik, L. Zehrer, A. Zepeda, B. Zimmermann,M. Ziolkowski, Z. Zongand F. Zuccarello
Data set attached as supplementary file
Evidence for a mixed mass composition at the ‘ankle’ in the cosmic-ray spectrum
No full textWe report a first measurement for ultrahigh energy cosmic rays of the correlation between the depth of shower maximum and the signal in the water Cherenkov stations of air-showers registered simultaneously by the fluorescence and the surface detectors of the Pierre Auger Observatory. Such a correlation measurement is a unique feature of a hybrid air-shower observatory with sensitivity to both the electromagnetic and muonic components. It allows an accurate determination of the spread of primary masses in the cosmic-ray flux. Up till now, constraints on the spread of primary masses have been dominated by systematic uncertainties. The present correlation measurement is not affected by systematics in the measurement of the depth of shower maximum or the signal in the water Cherenkov stations. The analysis relies on general characteristics of air showers and is thus robust also with respect to uncertainties in hadronic event generators. The observed correlation in the energy range around the ‘ankle’ at lg(E/eV)=18.5–19.0lg(E/eV)=18.5–19.0 differs significantly from expectations for pure primary cosmic-ray compositions. A light composition made up of proton and helium only is equally inconsistent with observations. The data are explained well by a mixed composition including nuclei with mass A>4A>4. Scenarios such as the proton dip model, with almost pure compositions, are thus disfavored as the sole explanation of the ultrahigh-energy cosmic-ray flux at Earth
Effective venue image retrieval using robust feature extraction and model constrained matching for mobile robot localization
No full textThis paper describes a novel system for mobile robot localization in an indoor environment, using concepts like homography and matching borrowed from the context of stereo and content-based image retrieval techniques (CBIR). To deal with variations with respect to viewpoint and camera positions, a group of points of interest (POI) is extracted to represent the image for robust matching. To cope with illumination changes, we propose to produce a contrast image for each video frame by using the root mean square strategy, thus all the POIs are extracted from the corresponding contrast images to provide perceptually consistent measurement of image content. To achieve effective image matching, modeling of robot behavior for model constrained matching is proposed, where normalized cross correlation is employed for local matching to determine corresponding POI pairs followed by homography based global optimization using RANSAC. Meanwhile, application of specific constraints also helps to exclude irrelevant frames in the training set to further improve the efficiency and robustness. The proposed approach has been successfully applied to the Robot Vision task for the ImageCLEF workshop, and the experimental results have fully demonstrated the high-quality performance of our approaches in terms of both precision and robustness. The system and approach outlined in this paper was ranked the second best in the optional task group in ImageCLEF 2009. In addition to demonstrating the merits of our approach in isolation, we also illustrate the benefits of our proposed approach in comparison with other submissions
Hybrid modeling and optimization of biological processes
No full textProß S. Hybrid modeling and optimization of biological processes. Bielefeld: Bielefeld University; 2013
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