An efficient, dense and long-range marker system for the guidance of the visually impaired
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Título: | An efficient, dense and long-range marker system for the guidance of the visually impaired |
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Autor/es: | Sáez Martínez, Juan Manuel | Lozano, Miguel Angel | Escolano, Francisco | Pita Lozano, Javier |
Grupo/s de investigación o GITE: | Laboratorio de Investigación en Visión Móvil (MVRLab) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Computer vision | Mobile vision | Visual markers | Visually impaired |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 18-ago-2020 |
Editor: | Springer Nature |
Cita bibliográfica: | Machine Vision and Applications. 2020, 31:57. https://doi.org/10.1007/s00138-020-01097-y |
Resumen: | In this paper, we address the problem of making a mobile/smartphone camera sensitive to distant fiducial markers. To this end, we carefully design a novel visual marker that is both dense and readable from large distances. The main novelty of the proposed marker is the combination of a quaternary color-based coding system with robust methods for reading the color patterns included in each frame once it is detected. These patterns include a CRC whose length grows linearly, whereas that of the message grows quadratically. Our experiments show that the proposed bundle marker-vision algorithm outperforms the alternatives in terms of distance and angle and also that it is very robust to changes in lighting conditions, thus making it a good intelligent system for guiding people with visual impairments in their day to day use of public transportation systems. |
Patrocinador/es: | This work is partially supported by the projects TIN2015-69077-P and RTI2018-096223-B-I00 of the Spanish Government and the grant INFO2016.08.ID+I.0019 from Instituto de Fomento de la Región de Murcia. |
URI: | http://hdl.handle.net/10045/109040 |
ISSN: | 0932-8092 (Print) | 1432-1769 (Online) |
DOI: | 10.1007/s00138-020-01097-y |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s00138-020-01097-y |
Aparece en las colecciones: | INV - MVRLab - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Saez_etal_2020_MachineVisionApplicat_final.pdf | Versión final (acceso restringido) | 2,24 MB | Adobe PDF | Abrir Solicitar una copia |
Saez_etal_2020_MachineVisionApplicat_preprint.pdf | Preprint (acceso abierto) | 4,21 MB | Adobe PDF | Abrir Vista previa |
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