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States of Matter Algorithm Applied to Pattern Detection

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Engineering Applications of Soft Computing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 129))

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Abstract

Pattern Detection (PD) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. PD involves two critical aspects: similarity measurement and search strategy. The simplest available PD method finds the best possible coincidence between the images through an exhaustive computation of the Normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy).

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Margarita-Arimatea Díaz-Cortés & Raúl Rojas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Erik Cuevas

Authors
  1. Margarita-Arimatea Díaz-Cortés
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  2. Erik Cuevas
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  3. Raúl Rojas
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Correspondence to Margarita-Arimatea Díaz-Cortés .

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Díaz-Cortés, MA., Cuevas, E., Rojas, R. (2017). States of Matter Algorithm Applied to Pattern Detection. In: Engineering Applications of Soft Computing. Intelligent Systems Reference Library, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-319-57813-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-57813-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57812-5

  • Online ISBN: 978-3-319-57813-2

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