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  • A massive advancement in image processing technologies supports a rapid on-line classification in well-defined pattern recognition tasks. Yet in general, there remains an urgent need for robust pre-processing techniques applicable independent of the task to be solved. Currently, two such approaches based on artificial neural networks are known from the literature, namely the so-called Extreme Learning Machines and the convolutional neural networks. In particular, the convolutional networks contain feature-detecting layers trained along with the rest of the network by means of the back-propagation algorithm. Another alternative for an automatic design and training of such pre-processors represents self-organization. Its main advantage consists in the much faster convergence during training. This work describes and compares several models of self-organizing networks with respect to their use for feature extraction and unsupervised building of preprocessing stage.
  • A massive advancement in image processing technologies supports a rapid on-line classification in well-defined pattern recognition tasks. Yet in general, there remains an urgent need for robust pre-processing techniques applicable independent of the task to be solved. Currently, two such approaches based on artificial neural networks are known from the literature, namely the so-called Extreme Learning Machines and the convolutional neural networks. In particular, the convolutional networks contain feature-detecting layers trained along with the rest of the network by means of the back-propagation algorithm. Another alternative for an automatic design and training of such pre-processors represents self-organization. Its main advantage consists in the much faster convergence during training. This work describes and compares several models of self-organizing networks with respect to their use for feature extraction and unsupervised building of preprocessing stage. (en)
Title
  • Self-organization for the Detection of Local Features
  • Self-organization for the Detection of Local Features (en)
skos:prefLabel
  • Self-organization for the Detection of Local Features
  • Self-organization for the Detection of Local Features (en)
skos:notation
  • RIV/00216208:11320/09:10080763!RIV11-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GD201/09/H057), S, Z(MSM0021620838)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 340700
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/09:10080763
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Local features; Pattern recognition; Self-organization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [48CDF858C1D2]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 18th Annual Conference of Doctoral Students - WDS 2009
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kukačka, Marek
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • MATFYZPRESS
https://schema.org/isbn
  • 978-80-7378-101-9
http://localhost/t...ganizacniJednotka
  • 11320
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