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O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; J. Lopez; A. Tovar; V. Valle; David Rotger; Petia Radeva |
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Cuantificacion tridimensional de la longitud de segmentos coronarios a partir de secuencias de ecografia intracoronaria |
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2003 |
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Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares |
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Sevilla (Spain) |
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BCNPCL @ bcnpcl @ RMF2003g |
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414 |
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Angel Sappa; Boris X. Vintimilla |
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Title |
Cost-Based Closed Contour Representations |
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2007 |
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Journal of Electronic Imaging, 16(2), 023009 (9 pages) |
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ADAS @ adas @ SaV2007 |
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803 |
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Oriol Rodriguez-Leor; J. Mauri; E. Fernandez-Nofrerias; J. Lopez; M. Gomez; V. Valle; David Rotger; Petia Radeva |
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Coronary arteries three-dimensional quantification using intravascular ultrasound and angiography |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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Vienna |
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MILAB |
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BCNPCL @ bcnpcl @ RMF2003d |
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411 |
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Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa |
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Title |
Corn kernel classification from few training samples |
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2023 |
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Artificial Intelligence in Agriculture |
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89-99 |
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This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types. |
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MSIAU |
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Admin @ si @ SVC2023 |
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3892 |
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Petia Radeva; Jordi Vitria |
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Corkinspect: Statistical Learning of Natural Material |
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2004 |
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Italian Beverage Technology, 13(38):11–18 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RaV2004b |
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514 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms |
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2007 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) |
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ADAS @ adas @ ASR2007b |
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922 |
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E. Pastor; A. Agueda; Juan Andrade; M. Muñoz; Y. Perez; E. Planas |
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Computing the rate of spread of linear flame fronts by thermal image processing |
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2006 |
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Fire Safety Journal, 41(8):569–579 |
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Admin @ si @ PAA2006 |
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679 |
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Author |
Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados |
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Title |
Computer Vision Tools for Visually Impaired Children Learning |
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2007 |
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DAG @ dag @ SFM2007a |
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891 |
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Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados |
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Computer Vision Tools for Visually Impaired Children Learning |
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2007 |
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DAG @ dag @ SFM2007b |
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892 |
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Oriol Rodriguez-Leor; Carlo Gatta; E. Fernandez-Nofrerias; Oriol Pujol; Neus Salvatella; C. Bosch; H. Tizon; Petia Radeva; J. Mauri |
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Computationally Efficient Image-based IVUS Pullbacks Gating |
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2008 |
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European Heart Journal, ESC Supplement, Munich, 2008, p. 775 |
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BCNPCL @ bcnpcl @ RGF2008 |
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1036 |
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M. Gonzalez-Audicana; Xavier Otazu; O. Fors; A. Seco |
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Comparison between Mallats and the trous discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images |
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2005 |
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International Journal of Remote Sensing, 26(3):595–614 (IF: 0.925) |
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CAT @ cat @ GOF2005 |
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530 |
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Xavier Otazu; Maria Vanrell; C. Alejandro Parraga |
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Colour induction effects are modelled by a low-level multiresolution wavelet framework |
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2008 |
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Perception 37(Suppl.): 107 |
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CAT @ cat @ OVP2008b |
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1055 |
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Angel Sappa; M.A. Garcia |
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Coarse-to-Fine Approximation of Range Images with Bounded Error Adaptive Triangular Meshes |
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2007 |
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Journal of Electronic Imaging, 16(2), 023010(11 pages) |
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ADAS @ adas @ SaG2007b |
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802 |
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A. Martinez; Jordi Vitria |
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Clustering in Image Space for Place Recognition and Visiual Annotations for Human-Robot Interaction. |
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2001 |
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IEEE Trans. on Systems, Man, and Cybernatics–Part B: Cybernetics, 31(5):669–682 (IF: 0.789) |
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BCNPCL @ bcnpcl @ MVi2001 |
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141 |
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Marçal Rusiñol |
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Classificació semàntica i visual de documents digitals |
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2019 |
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Revista de biblioteconomia i documentacio |
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75-86 |
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Se analizan los sistemas de procesamiento automático que trabajan sobre documentos digitalizados con el objetivo de describir los contenidos. De esta forma contribuyen a facilitar el acceso, permitir la indización automática y hacer accesibles los documentos a los motores de búsqueda. El objetivo de estas tecnologías es poder entrenar modelos computacionales que sean capaces de clasificar, agrupar o realizar búsquedas sobre documentos digitales. Así, se describen las tareas de clasificación, agrupamiento y búsqueda. Cuando utilizamos tecnologías de inteligencia artificial en los sistemas de
clasificación esperamos que la herramienta nos devuelva etiquetas semánticas; en sistemas de agrupamiento que nos devuelva documentos agrupados en clusters significativos; y en sistemas de búsqueda esperamos que dada una consulta, nos devuelva una lista ordenada de documentos en función de la relevancia. A continuación se da una visión de conjunto de los métodos que nos permiten describir los documentos digitales, tanto de manera visual (cuál es su apariencia), como a partir de sus contenidos semánticos (de qué hablan). En cuanto a la descripción visual de documentos se aborda el estado de la cuestión de las representaciones numéricas de documentos digitalizados
tanto por métodos clásicos como por métodos basados en el aprendizaje profundo (deep learning). Respecto de la descripción semántica de los contenidos se analizan técnicas como el reconocimiento óptico de caracteres (OCR); el cálculo de estadísticas básicas sobre la aparición de las diferentes palabras en un texto (bag-of-words model); y los métodos basados en aprendizaje profundo como el método word2vec, basado en una red neuronal que, dadas unas cuantas palabras de un texto, debe predecir cuál será la
siguiente palabra. Desde el campo de las ingenierías se están transfiriendo conocimientos que se han integrado en productos o servicios en los ámbitos de la archivística, la biblioteconomía, la documentación y las plataformas de gran consumo, sin embargo los algoritmos deben ser lo suficientemente eficientes no sólo para el reconocimiento y transcripción literal sino también para la capacidad de interpretación de los contenidos. |
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DAG; 600.084; 600.135; 600.121; 600.129 |
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