Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Germán Martín García"'
Autor:
Roberto Escala-Cornejo, Alejandro Olivares-Hernández, María García Muñoz, Luis Figuero-Pérez, Javier Martín Vallejo, José Pablo Miramontes-González, Magdalena Sancho de Salas, María Asunción Gómez Muñoz, Raquel Seijas Tamayo, Germán Martín García, Emilio Fonseca Sánchez, César Rodríguez-Sánchez
Publikováno v:
EMJ Oncology.
Background: A surrogate classification of breast cancer (BC) molecular subtypes based on immunohistochemistry (IHC) was established at the 13th St. Gallen International Breast Cancer Consensus (SG-BCC). The most controversial point of discussion was
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b9e9fa59c812dc0a94b331f3e0caf4b
https://doi.org/10.33590/emjoncol/22-00100
https://doi.org/10.33590/emjoncol/22-00100
Autor:
Ronald P. A. Petrick, Arnaud Charzoule, Germano Veiga, Francesco Rovida, Bjarne Grossmann, Volker Krueger, Germán Martín García, Sven Behnke, César Toscano, Matthew Crosby
Publikováno v:
Robotics and Computer-Integrated Manufacturing
In recent years, cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through (a) an integration of the robots with the Manufacturing Execution System (MES), (b) a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e68727df120e43e131b72bce7dddf0a
Publikováno v:
Intelligent Autonomous Systems 15 ISBN: 9783030013691
IAS
IAS
Individualized manufacturing of cars requires kitting: the collection of individual sets of part variants for each car. This challenging logistic task is frequently performed manually by warehouseman. We propose a mobile manipulation robotic system f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a03401830e7a02159d2da0cd406bc675
https://doi.org/10.1007/978-3-030-01370-7_66
https://doi.org/10.1007/978-3-030-01370-7_66
Autor:
Christian Lenz, Michael Schreiber, Max Schwarz, Seongyong Koo, Sven Behnke, Arul Selvam Periyasamy, Germán Martín García
Publikováno v:
2018 IEEE International Conference on Robotics and Automation (ICRA)
ICRA
ICRA
Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30fe3852d95692ce1114ef2b719ad417
Autor:
Seongyong Koo, Germán Martín García, Grzegorz Ficht, Sven Behnke, Martin Raak, Dmytro Pavlichenko
Publikováno v:
CASE
ResearcherID
ResearcherID
The feeding of parts from bulk supply to production lines is a common but still challenging task in industrial automation. In this paper, we present a part feeding system that has two objectives: first, it needs to be easy reconfigurable to handle ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20f152430c8e4051f776369d490ef04a
https://doi.org/10.1109/coase.2017.8256073
https://doi.org/10.1109/coase.2017.8256073
Publikováno v:
KI - Künstliche Intelligenz. 29:75-81
In this paper, we summarize our project work of the last two years, where we addressed the tasks of visually exploring a scene with visual attention mechanisms based on saliency computation, and of locating unknown objects in the environment. The lat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fc5e9a0aea4cdf970816c94487bb3fb
https://doi.org/10.1007/s13218-014-0337-9
https://doi.org/10.1007/s13218-014-0337-9
Publikováno v:
KI - Künstliche Intelligenz. 27:267-272
We present an attention-based approach for the detection of unknown objects in a 3D environment. The ability to address individual objects in the environment without having previous knowledge about their properties or their identity is one important
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcf0b2630223148652683d9fa22eee49
https://doi.org/10.1007/s13218-013-0256-1
https://doi.org/10.1007/s13218-013-0256-1
Autor:
Simone Frintrop, Farzad Husain, Germán Martín García, Sven Behnke, Hannes Schulz, Carme Torras
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
ICPR
Recercat. Dipósit de la Recerca de Catalunya
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
instname
ICPR
Recercat. Dipósit de la Recerca de Catalunya
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Trabajo presentado a la 23rd International Conference on Pattern Recognition, celebrada en Cancún (México) del 5 al 8 de diciembre de 2016.
Reliable object discovery in realistic indoor scenes is a necessity for many computer vision and servic
Reliable object discovery in realistic indoor scenes is a necessity for many computer vision and servic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb1345e0a0df08deb54487e97b25204b
https://hdl.handle.net/2117/105856
https://hdl.handle.net/2117/105856
Publikováno v:
Cognitive processing. 18(2)
We present a computational framework for attention-guided visual scene exploration in sequences of RGB-D data. For this, we propose a visual object candidate generation method to produce object hypotheses about the objects in the scene. An attention
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c874fe52ceea9e8bf9b57fd0bc2a1511
https://pubmed.ncbi.nlm.nih.gov/28154991
https://pubmed.ncbi.nlm.nih.gov/28154991
Publikováno v:
CVPR
In this paper, we show that the seminal, biologically-inspired saliency model by Itti et al. [21] is still competitive with current state-of-the-art methods for salient object segmentation if some important adaptions are made. We show which changes a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2242c7b088ce8279b66eba59f4731f8b
https://doi.org/10.1109/cvpr.2015.7298603
https://doi.org/10.1109/cvpr.2015.7298603