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Statistical Pattern Recognition: The State of the Art

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Image Analysis and Processing II

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Abstract

Objects or events in the universe are perceived by biological systems as patterns. Pattern recognition is a process which assigns these sensory stimuli into perceptually meaningful categories. For the last four decades a considerable effort has been made to simulate the human pattern recognition capabilities by a machine. This quest for automation of pattern recognition processes is primarily driven by applications in computer vision for flexible manufacturing, speech recognition, text recognition, remote sensing, medicine and others. In computer vision for robots, for instance, the pattern recognition task may involve identification of object shape. In speech recognition the object categories may be words, phonemes or diphones and the sensory data on which classification is based could be vector quantised speech signal. In text recognition the objects of interest are characters and their groups forming words. Object categories in remote sensing relate to land cover and the sensory data are reflected energies in several spectral channels of the electromagnetic spectrum.

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Author information

Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Surrey, Guildford, GU2 5XH, UK

    Josef Kittler

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  1. Josef Kittler
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Editor information

Editors and Affiliations

  1. Pavia University, Pavia, Italy

    V. Cantoni

  2. Palermo University, Palermo, Italy

    V. Di Gesù

  3. Rome University, Rome, Italy

    S. Levialdi

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© 1988 Plenum Press, New York

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Kittler, J. (1988). Statistical Pattern Recognition: The State of the Art. In: Cantoni, V., Di Gesù, V., Levialdi, S. (eds) Image Analysis and Processing II. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1007-5_5

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  • DOI: https://doi.org/10.1007/978-1-4613-1007-5_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8289-1

  • Online ISBN: 978-1-4613-1007-5

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