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180 results on '"Gatta, Carlo"'

1. Bag of Negatives for Siamese Architectures

Author

Gajic, Bojana, Amato, Ariel, Baldrich, Ramon, and Gatta, Carlo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy., Comment: accepted for BMVC2019

Published
2019

3. Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

Author

Romero, Adriana, Gatta, Carlo, and Camps-Valls, Gustau

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyper-spectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layer-wise unsupervised pre-training coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution (VHR), or land-cover classification from multi- and hyper-spectral images. The proposed algorithm clearly outperforms standard Principal Component Analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels, and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single layers variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.

Published
2015
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4. FitNets: Hints for Thin Deep Nets

Author

Romero, Adriana, Ballas, Nicolas, Kahou, Samira Ebrahimi, Chassang, Antoine, Gatta, Carlo, and Bengio, Yoshua

Subjects
Computer Science - Learning, Computer Science - Neural and Evolutionary Computing
Abstract

While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network.

Published
2014

5. No more meta-parameter tuning in unsupervised sparse feature learning

Author

Romero, Adriana, Radeva, Petia, and Gatta, Carlo

Subjects
Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.

Published
2014

6. Do We Really Need All These Neurons?

Author

Romero, Adriana, Gatta, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sanches, João M., editor, Micó, Luisa, editor, and Cardoso, Jaime S., editor

Published
2013
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7. Robust and Accurate Diaphragm Border Detection in Cardiac X-Ray Angiographies

Author

Petkov, Simeon, Romero, Adriana, Suarez, Xavier Carrillo, Radeva, Petia, Gatta, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2013
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8. Ultrasound Despeckle Methods

Author

Balocco, Simone, Gatta, Carlo, Ferré, Josepa Mauri, Radeva, Petia, Sanches, Joao Miguel, editor, Laine, Andrew F., editor, and Suri, Jasjit S., editor

Published
2012
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9. Accurate and Robust Fully-Automatic QCA: Method and Numerical Validation

Author

Hernández-Vela, Antonio, Gatta, Carlo, Escalera, Sergio, Igual, Laura, Martin-Yuste, Victoria, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fichtinger, Gabor, editor, Martel, Anne, editor, and Peters, Terry, editor

Published
2011
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10. A Holistic Approach for the Detection of Media-Adventitia Border in IVUS

Author

Ciompi, Francesco, Pujol, Oriol, Gatta, Carlo, Carrillo, Xavier, Mauri, Josepa, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fichtinger, Gabor, editor, Martel, Anne, editor, and Peters, Terry, editor

Published
2011
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11. Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation

Author

Balocco, Simone, Gatta, Carlo, Ciompi, Francesco, Pujol, Oriol, Carrillo, Xavier, Mauri, Josepa, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Vitrià, Jordi, editor, Sanches, João Miguel, editor, and Hernández, Mario, editor

Published
2011
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12. Non-rigid Multi-modal Registration of Coronary Arteries Using SIFTflow

Author

Gatta, Carlo, Balocco, Simone, Martin-Yuste, Victoria, Leta, Ruben, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Vitrià, Jordi, editor, Sanches, João Miguel, editor, and Hernández, Mario, editor

Published
2011
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13. Automatic Branching Detection in IVUS Sequences

Author

Alberti, Marina, Gatta, Carlo, Balocco, Simone, Ciompi, Francesco, Pujol, Oriol, Silva, Joana, Carrillo, Xavier, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Vitrià, Jordi, editor, Sanches, João Miguel, editor, and Hernández, Mario, editor

Published
2011
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14. Real-Time Gating of IVUS Sequences Based on Motion Blur Analysis: Method and Quantitative Validation

Author

Gatta, Carlo, Balocco, Simone, Ciompi, Francesco, Hemetsberger, Rayyan, Leor, Oriol Rodriguez, Radeva, Petia, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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16. Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

Author

Balocco, Simone, Gatta, Carlo, Ciompi, Francesco, Wahle, Andreas, Radeva, Petia, Carlier, Stephane, Unal, Gozde, Sanidas, Elias, Mauri, Josepa, Carillo, Xavier, Kovarnik, Tomas, Wang, Ching-Wei, Chen, Hsiang-Chou, Exarchos, Themis P., Fotiadis, Dimitrios I., Destrempes, François, Cloutier, Guy, Pujol, Oriol, Alberti, Marina, Mendizabal-Ruiz, E. Gerardo, Rivera, Mariano, Aksoy, Timur, Downe, Richard W., and Kakadiaris, Ioannis A.

Published
2014
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17. Multi-scale Stacked Sequential Learning

Author

Pujol, Oriol, Puertas, Eloi, Gatta, Carlo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Benediktsson, Jón Atli, editor, and Roli, Fabio, editor

Published
2009
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18. Enhancing In-Vitro IVUS Data for Tissue Characterization

Author

Ciompi, Francesco, Pujol, Oriol, Rodriguez Leor, Oriol, Gatta, Carlo, Serrano Vida, Angel, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Araujo, Helder, editor, Mendonça, Ana Maria, editor, Pinho, Armando J., editor, and Torres, María Inés, editor

Published
2009
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19. Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography

Author

Gatta, Carlo, Gomez Valencia, Juan Diego, Ciompi, Francesco, Rodriguez Leor, Oriol, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Araujo, Helder, editor, Mendonça, Ana Maria, editor, Pinho, Armando J., editor, and Torres, María Inés, editor

Published
2009
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20. Robust Image-Based IVUS Pullbacks Gating

Author

Gatta, Carlo, Pujol, Oriol, Leor, Oriol Rodriguez, Ferre, Josepa Mauri, Radeva, Petia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Metaxas, Dimitris, editor, Axel, Leon, editor, Fichtinger, Gabor, editor, and Székely, Gábor, editor

Published
2008
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29. A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast

Author

Provenzi, Edoardo, Gatta, Carlo, Fierro, Massimo, and Rizzi, Alessandro

Subjects
False color imaging -- Analysis, False color imaging -- Methods, Image processing -- Analysis
Abstract

Starting from the revolutionary Retinex by Land and McCann, several further perceptually inspired color correction models have been developed with different aims, e.g., reproduction of color sensation, robust features recognition, and enhancement of color images. Such models have a differential, spatially variant, and nonlinear nature, and they can coarsely be distinguished between white-patch (WP) and gray-world (GW) algorithms. In this paper, we show that the combination of a pure WP algorithm (Random Spray Retinex (RSR)) and an essentially GW one (Automatic Color Equalization (ACE)) leads to a more robust and better performing model (RACE). The choice of RSR and ACE follows from the recent identification of a unified spatially variant approach for both algorithms. Mathematically, the originally distinct nonlinear and differential mechanisms of RSR and ACE have been fused using the spray technique and local average operations. The investigation of RACE allowed us to put in evidence a common drawback of differential models: corruption of uniform image areas. To overcome this intrinsic defect, we devised a local and global contrast-based and image-driven regulation mechanism that has a general applicability to perceptually inspired color correction algorithms. Tests, comparisons, and discussions are presented. Index Terms--Color image enhancement, image stabilization, contrast-based regulation mechanism, Retinex, ACE.

Published
2008

30. Training strategies for efficient deep image retrieval

Author

Baldrich i Caselles, Ramon, Gatta, Carlo, Gajić, Bojana, Baldrich i Caselles, Ramon, Gatta, Carlo, and Gajić, Bojana

Abstract

En aquesta tesi ens centrem en la recuperació i re-identificació d'imatges. L'entrenament de xarxes neuronals profundes usant funcions de pèrdua basades en rànquing ha esdevingut un estàndard de facto per a les tasques de recuperació i re-identificació. Hi analitzem i aportem propostes de respostes a tres qüestions principals: 1) Quines són les estratègies més rellevants dels mètodes de l'estat de l'art i com es poden combinar per obtenir un millor rendiment? 2) Es pot realitzar un mostreig de mostres negatives restrictiu de manera eficient (O(1)) mentre es proporciona un rendiment millorat respecte al mostreig aleatori simple? 3) Es poden aconseguir objectius de reconeixement i recuperació mitjançant una funció de pèrdua basada en el reconeixement? En primer lloc, en el capítol 4 analitzem la importància d'algunes estratègies de l'estat de l'art relacionades amb la formació d'un model d'aprenentatge profund que abasta l'augment d'imatges, l'arquitectura vertebral i la mineria de tripletes restrictives. A continuació, combinem les millors estratègies per dissenyar una arquitectura profunda senzilla, a més d'una metodologia d'entrenament per a una identificació de persones efectiva i d'alta qualitat. Avaluem àmpliament cada opció de disseny, donant lloc a una llista de bones pràctiques per a la re-identificació de persones. Seguint aquestes pràctiques, el nostre enfocament supera l'estat de l'art, inclosos mètodes més complexos amb components auxiliars, de forma amplia en quatre conjunts de dades de referència. També proporcionem una anàlisi qualitativa de la nostra representació entrenada que indica que, tot i ser compacta, és capaç de captar informació de regions focalitzades i discriminatives, d'una manera semblant a un mecanisme d'atenció implícita. En segon lloc, al capítol 5 abordem el problema del mostreig de mostres negatives restrictiu quan s'entrena un model amb funcions del tipus pèrdua per tripletes. En aquest capítol presentem"Bag of Negatives (BoN)", un, En esta tesis nos centramos en la recuperación y re-identificación de imágenes. El entrenamiento de redes neuronales profundas usando funciones de pérdida basadas en ranking se ha convertido en un estándar de facto para las tareas de recuperación y re-identificación. Analizamos y aportamos propuestas de respuestas a tres cuestiones principales: 1) ¿Cuáles son las estrategias más relevantes de los métodos del estado del arte y cómo se pueden combinar para obtener un mejor rendimiento? 2) ¿Se puede realizar unmuestreo de muestras negativas restrictivo de manera eficiente (O(1)) mientras se proporciona un rendimiento mejorado respecto almuestreo aleatorio simple? 3) ¿Se pueden conseguir objetivos de reconocimiento y recuperación mediante una función de pérdida basada en el reconocimiento? En primer lugar, en el capítulo 4 analizamos la importancia de algunas estrategias del estado del arte relacionadas con la formación de un modelo de aprendizaje profundo que abarca el aumento de imágenes, la arquitectura vertebral y la minería de tripletas restrictivas. A continuación, combinamos las mejores estrategias para diseñar una arquitectura profunda sencilla, además de una metodología de entrenamiento para una identificación de personas efectiva y de alta calidad. Evaluamos ampliamente cada opción de diseño, dando lugar a una lista de buenas prácticas para la re-identificación de personas. Siguiendo estas prácticas, nuestro enfoque supera el estado del arte, incluidos métodos más complejos con componentes auxiliares, de forma amplia en cuatro conjuntos de datos de referencia. También proporcionamos un análisis cualitativo de nuestra representación entrenada que indica que, a pesar de ser compacta, es capaz de captar información de regiones focalizadas y discriminativas, de una manera similar a un mecanismo de atención implícita. En segundo lugar, el capítulo 5 abordamos el problema del muestreo demuestras negativas restrictivo cuando se entrena un modelo con funciones del tip, In this thesis we focus on image retrieval and re-identification. Training a deep architecture using a ranking loss has become standard for the retrieval and re-identification tasks. We analyze and propose answers on three main issues: 1) What are the most relevant strategies of state-of-the-art methods and how can they be combined in order to obtain a better performance? 2) Can hard negative sampling be performed efficiently (O(1)) while providing improved performance over naïve random sampling? 3) Can recognition and retrieval objectives be achieved by using a recognition-based loss? First, in chapter 4 we analyze the importance of some state of the art strategies related to the training of a deep model such as image augmentation, backbone architecture and hard triplet mining. We then combine the best strategies to design a simple deep architecture plus a training methodology for effective and high quality person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism. Second, in chapter 5 we address the problem of hard negative sampling when training a model with triplet-like loss. In this chapter we present Bag of Negatives (BoN), a fast hard negative mining method, that provides a set, triplet or pair of potentially relevant training samples. BoN is an efficient method that selects a bag of hard negatives based on a novel online hashing strategy. We show the superiority of BoN against state-of-the-art hard negative mining methods in terms of accuracy and training time over three lar, Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtica

Published
2021

31. A multiscale framework for spatial gamut mapping

Author

Farup, Ivar, Gatta, Carlo, and Rizzi, Alessandro

Subjects
Dithering -- Methods, Image processing -- Methods, Image processing -- Equipment and supplies, Image processing -- Usage, Image processor, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A framework for multiscale spatial gamut mapping algorithm (GMA) is presented. Findings reveal that multilevel gamut mapping approach outperforms spatially invariant approaches.

Published
2007

36. Automatic Branching Detection in IVUS Sequences

Author

Alberti, Marina, primary, Gatta, Carlo, additional, Balocco, Simone, additional, Ciompi, Francesco, additional, Pujol, Oriol, additional, Silva, Joana, additional, Carrillo, Xavier, additional, and Radeva, Petia, additional

Published
2011
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43. Relation between plaque type, plaque thickness, blood shear stress, and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound

Author

Balocco, Simone, Gatta, Carlo, Alberti, Marina, Carrillo, Xavier, Rigla, Juan, Radeva, Petia, and Universitat de Barcelona

Subjects
Blood vessels, Imatges mèdiques, Ultrasonics in medicine, Ultrasons en medicina, Vasos sanguinis, Imaging systems in medicine
Abstract

Purpose: Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries. Methods: First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound(IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations. Results: The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall. Conclusions: Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed.

Published
2014

45. Processing of extremely high-resolution LiDAR and RGB data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

Author

Campos-Taberner, Manuel, Romero-Soriano, Adriana, Gatta, Carlo, Camps-Valls, Gustau, Lagrange, Adrien, Le Saux, Bertrand, Beaupere, Anne, Boulch, Alexandre, Chan-Hon-Tong, Adrien, Herbin, Stephane, Randrianarivo, Hicham, Ferecatu, Marin, Shimoni, Michal, Moser, Gabriele, Tuia, Devis, Campos-Taberner, Manuel, Romero-Soriano, Adriana, Gatta, Carlo, Camps-Valls, Gustau, Lagrange, Adrien, Le Saux, Bertrand, Beaupere, Anne, Boulch, Alexandre, Chan-Hon-Tong, Adrien, Herbin, Stephane, Randrianarivo, Hicham, Ferecatu, Marin, Shimoni, Michal, Moser, Gabriele, and Tuia, Devis

Abstract

In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technicalcommittees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].

Published
2016

46. Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

Author

Campos-Taberner, Manuel, primary, Romero-Soriano, Adriana, additional, Gatta, Carlo, additional, Camps-Valls, Gustau, additional, Lagrange, Adrien, additional, Le Saux, Bertrand, additional, Beaupere, Anne, additional, Boulch, Alexandre, additional, Chan-Hon-Tong, Adrien, additional, Herbin, Stephane, additional, Randrianarivo, Hicham, additional, Ferecatu, Marin, additional, Shimoni, Michal, additional, Moser, Gabriele, additional, and Tuia, Devis, additional

Published
2016
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47. Editorial

Author

Lee, Su-Lin, primary, Balocco, Simone, additional, Gatta, Carlo, additional, Demirci, Stefanie, additional, and Tangen, Geir Arne, additional

Published
2016
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