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Your search keyword '"Bianco, Simone"' showing total 31 results
Start Over Author "Bianco, Simone" Publisher elsevier bv
31 results on '"Bianco, Simone"'

6. ENRICH: multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry

Author

Marelli, D, Morelli, L, Farella, E, Bianco, S, Ciocca, G, Remondino, F, Marelli, Davide, Morelli, Luca, Farella, Elisa Mariarosaria, Bianco, Simone, Ciocca, Gianluigi, Remondino, Fabio, Marelli, D, Morelli, L, Farella, E, Bianco, S, Ciocca, G, Remondino, F, Marelli, Davide, Morelli, Luca, Farella, Elisa Mariarosaria, Bianco, Simone, Ciocca, Gianluigi, and Remondino, Fabio

Abstract

The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly. Moreover, it is often difficult to acquire real data for a given application domain that is sufficiently representative and heterogeneous in terms of scene representation, acquisition conditions, point of view, etc. To overcome the limitations of available datasets, this paper presents a new synthetic, multi-purpose dataset called ENRICH for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images rendered with different lighting conditions, camera orientations, scales, and fields of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. We show the usefulness of the proposed dataset on several examples of photogrammetry and computer vision-related tasks such as: evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. We make ENRICH publicly available at: https://github.com/davidemarelli/ENRICH.

Published
2023

12. Globally local: Hyper-local modeling for accurate forecast of COVID-19

Author

Gopalakrishnan, Vishrawas, primary, Pethe, Sayali, additional, Kefayati, Sarah, additional, Srinivasan, Raman, additional, Hake, Paul, additional, Deshpande, Ajay, additional, Liu, Xuan, additional, Hoang, Etter, additional, Davila, Marbelly, additional, Bianco, Simone, additional, and Kaufman, James H., additional

Published
2021
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13. A defective viral genome strategy elicits broad protective immunity against respiratory viruses

Author

Xiao, Yinghong, primary, Lidsky, Peter V., additional, Shirogane, Yuta, additional, Aviner, Ranen, additional, Wu, Chien-Ting, additional, Li, Weiyi, additional, Zheng, Weihao, additional, Talbot, Dale, additional, Catching, Adam, additional, Doitsh, Gilad, additional, Su, Weiheng, additional, Gekko, Colby E., additional, Nayak, Arabinda, additional, Ernst, Joel D., additional, Brodsky, Leonid, additional, Brodsky, Elia, additional, Rousseau, Elsa, additional, Capponi, Sara, additional, Bianco, Simone, additional, Nakamura, Robert, additional, Jackson, Peter K., additional, Frydman, Judith, additional, and Andino, Raul, additional

Published
2021
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25. Beam test results of pixel triggerless prototypes for the P¯ANDA MVD

Author

Calvo, Daniela, primary, De Remigis, Paolo, additional, Filippi, Alessandra, additional, Mazza, Giovanni, additional, Rivetti, Angelo, additional, Wheadon, Richard, additional, De Mori, Francesca, additional, Marcello, Simonetta, additional, Zotti, Laura, additional, Bianco, Simone, additional, Zaunick, Hans-Georg, additional, Brinkmann, Kai-Thomas, additional, Quagli, Tommaso, additional, and Schnell, Robert, additional

Published
2013
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26. Automatic Color Constancy Algorithm Selection and Combination

Author

Bianco, S, Ciocca, G, Cusano, C, Schettini, R, BIANCO, SIMONE, CIOCCA, GIANLUIGI, SCHETTINI, RAIMONDO, Bianco, S, Ciocca, G, Cusano, C, Schettini, R, BIANCO, SIMONE, CIOCCA, GIANLUIGI, and SCHETTINI, RAIMONDO

Abstract

In this work, we investigate how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images. We show how these properties can be used to drive, given a set of illuminant estimation algorithms, the selection of the best algorithm for a given image. The algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features. The features represent the image content in terms of low-level visual properties. The trees are trained to select the algorithm that minimizes the expected error in illuminant estimation. We also designed a combination strategy that estimates the illuminant as a weighted sum of the different algorithms' estimations. Experimental results on the widely used Ciurea and Funt dataset demonstrate the effectiveness of our approach.

Published
2010

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