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Model-Based Pattern Recognition

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Pattern Recognition and String Matching

Part of the book series: Combinatorial Optimization ((COOP,volume 13))

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Abstract

Recognition and processing of multi-dimensional data (or set of spatially related objects) is more accurate and efficient if we take into account all interdependencies between single objects. Objects to be processed like for example multi-spectral pixels in a digitized image, are often mutually dependent (e.g., correlated) with a dependency degree related to a distance between two objects in their corresponding data space. These relations can be incorporated into a pattern recognition process through appropriate multidimensional data model. If such a model is probabilistic we can use consistent Bayesian framework for solving many pattern recognition tasks. Data models are simultaneously useful to specify natural constraints and general assumptions about the physical world and a data capturing process hence they are essential in many data modelling or analytical procedures such as classification, segmentation, discontinuity detection, restoration, enhancement and scene analysis in general. Features derived from multi-dimensional data models are information preserving in the sense that they can be used to synthesize data spaces closely resembling original measurement data space as can be illustrated on the texture modelling application in the section 2.4.

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Authors and Affiliations

  1. Department of Pattern Recognition, Institute of Information Theory and Automation, Academy of Sciences CR, Prague, Czech Republic

    Michal Haindl

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  1. Michal Haindl
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Editors and Affiliations

  1. University of Wisconsin — Green Bay, Green Bay, WI, USA

    Dechang Chen

  2. The George Washington University, Washington DC, USA

    Xiuzhen Cheng

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© 2003 Kluwer Academic Publishers

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Haindl, M. (2003). Model-Based Pattern Recognition. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_9

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  • DOI: https://doi.org/10.1007/978-1-4613-0231-5_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7952-2

  • Online ISBN: 978-1-4613-0231-5

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