Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

Mora de Sambricio, Javier; Gallego Galán, Ángel; Otero Marnotes, Andres; Torre Arnanz, Eduardo de la y Riesgo Alcaide, Teresa (2013). Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform. En: "Conference on Design and Architectures for Signal and Image Processing (DASIP)", 08/10/2013 - 10/10/2013, Cagliari, Italy. pp..

Descripción

Título: Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform
Autor/es:
  • Mora de Sambricio, Javier
  • Gallego Galán, Ángel
  • Otero Marnotes, Andres
  • Torre Arnanz, Eduardo de la
  • Riesgo Alcaide, Teresa
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Conference on Design and Architectures for Signal and Image Processing (DASIP)
Fechas del Evento: 08/10/2013 - 10/10/2013
Lugar del Evento: Cagliari, Italy
Título del Libro: Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform
Fecha: 2013
Materias:
Palabras Clave Informales: Evolvable hardware; evolutionaryalgorithms; adaptivesystems; imagefilter; referenceimage; camera
Escuela: Centro de Electrónica Industrial (CEI) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

Más información

ID de Registro: 29511
Identificador DC: http://oa.upm.es/29511/
Identificador OAI: oai:oa.upm.es:29511
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6661538&queryText%3DNoise-agnostic+adaptive+image+filtering+without+training+references+on+an+evolvable+hardware+platform
Depositado por: Memoria Investigacion
Depositado el: 27 Abr 2015 18:45
Ultima Modificación: 27 Abr 2015 18:45
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