A study on backpropagation networks for parameter estimation from grey-scale images

Tian-Jin Feng, Z. Houkes, M.J. Korsten, L.J. Spreeuwers

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    Abstract

    A large number of experiments have been done on the basic research of parameter estimation from images with neural networks. To obtain a better estimation accuracy of parameters and to decrease needed storage space and computation time, the architecture of networks, the effective learning rate and momentum, and the selection of training set are investigated. A comparison of network performance to that of the least squares estimator is made. The internal representations in trained networks, i.e. input-to-hidden weight maps or measuring models, which include statistical features of training images and have a clear physical and geometrical meaning, and the internal components of output parameters given by outputs of hidden neurons are presented
    Original languageEnglish
    Title of host publicationProceedings of the 1991 IEEE International Joint Conference on Neural Networks, IJCNN'91
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages331-336
    Number of pages6
    Volume1
    ISBN (Print)0-7803-0227-3
    DOIs
    Publication statusPublished - 18 Nov 1991
    Event1991 IEEE International Conference on Neural Networks, ICNN 1991 - Singapore, Singapore
    Duration: 18 Nov 199121 Nov 1991

    Conference

    Conference1991 IEEE International Conference on Neural Networks, ICNN 1991
    Abbreviated titleICNN
    CountrySingapore
    CitySingapore
    Period18/11/9121/11/91

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    Feng, T-J., Houkes, Z., Korsten, M. J., & Spreeuwers, L. J. (1991). A study on backpropagation networks for parameter estimation from grey-scale images. In Proceedings of the 1991 IEEE International Joint Conference on Neural Networks, IJCNN'91 (Vol. 1, pp. 331-336). Piscataway, NJ: IEEE. https://doi.org/10.1109/IJCNN.1991.170423