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Artificial Life and Robotics
Erschienen in: Artificial Life and Robotics 4/2015

01.12.2015 | Original Article

Image recognition using adaptive fuzzy neural network based on lifting scheme of wavelet

verfasst von: Chia-Nan Ko, Cheng-Ming Lee

Erschienen in: Artificial Life and Robotics | Ausgabe 4/2015

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Abstract

This article proposes an adaptive fuzzy neural network (AFNN) based on lifting scheme of wavelets to recognize image with noise/blur. In the research, first, the image with noise/blur is completed through the gray level transformation to discrete space; then the discrete sequence is classified using lifting wavelet transformation. The image processing is performed by the AFNN in which a time-varying adaptive learning algorithm is adopted. The root-mean-square error is used to evaluate the efficiency of image recognition. Meanwhile, comparisons of the lifting adaptive fuzzy neural network with fuzzy neural network are made to verify the performance of the proposed adaptive fuzzy neural network.
Vorheriger Artikel Detection of periodic patterns in microarray data reveals novel oscillating transcripts of biological rhythms in Ciona intestinalis
Nächster Artikel Development of impact-type rotary actuator and application for MEMS microrobot by bare chip IC of hardware neuron model

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Metadaten
Titel
Image recognition using adaptive fuzzy neural network based on lifting scheme of wavelet
verfasst von
Chia-Nan Ko
Cheng-Ming Lee
Publikationsdatum
01.12.2015
Verlag
Springer Japan
Erschienen in
Artificial Life and Robotics / Ausgabe 4/2015
Print ISSN: 1433-5298
Elektronische ISSN: 1614-7456
DOI
https://doi.org/10.1007/s10015-015-0242-9

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