Sistema de Visión Subacuático Inalámbrico Usando un Algoritmo de Compresión Progresivo con Región de Interés

• Autores: Eduardo M. Rubino, D. Centelles, Jorge Sales Gil, José V. Martí, Raúl Marín , Alberto J. Alvares, Pedro José Sanz Valero
• Localización: Revista iberoamericana de automática e informática industrial ( RIAI ), ISSN-e 1697-7920, Vol. 15, Nº. 1, 2018, págs. 46-57
• Idioma: español
• DOI: 10.4995/riai.2017.8953
• Títulos paralelos:
  • Underwater Wireless Vision System Using Progressive Image Compression and Region of Interest
• Enlaces
  • Texto completo
• Resumen
  • español

    La creciente demanda en todo el mundo de sistemas de intervención submarina en diversos dominios de aplicación requiere sistemas más versátiles y económicos. Empleando un sistema de comunicación inalámbrica, los robots semiautónomos supervisados disponen de libertad de movimientos y, al mismo tiempo, permiten al operador obtener información visual y supervisar la intervención. Por otro lado, la velocidad de transmisión de datos típica de los canales inalámbricos submarinos es, en general, muy limitada, siendo necesaria la aplicación de técnicas de compresión avanzadas. En este artículo se presenta principalmente el algoritmo DEBT (Depth Embedded Block Tree) para la compresión progresiva de imágenes con región de interés (ROI). Los resultados demuestran ventajas con respecto a otros algoritmos de compresión, y la posibilidad de ejecución del algoritmo en tiempo real en ordenadores embebidos de bajo consumo basados en ARM.

  • English

    The increasing demand for underwater robotic intervention systems around the world in several application domains requires more versatile and inexpensive systems. By using a wireless communication system, supervised semi-autonomous robots have freedom of movement and, at the same time, allows the operator to get camera feedback and supervise the intervention. On the otherhand, the typical data rate of the wireless submarine channels is generally very limited, requiring the application of advanced compression techniques. In this paper we present the DEBT (Depth Embedded Block Tree) algorithm for the progressive compressionof images with region of interest (ROI). The results demonstrate advantages with other compression algorithms, and the possibilityof executing the algorithm in real time on ARM-based embedded low-power computers.

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