Abstract
A sequence being, perhaps, the simplest structure which for a number of reasons is best suited for us to use to start our talk on structural recognition. Even in such a simple special case it can be shown how to recognise a complex object which consists of many parts and how the knowledge of relations between the parts contributes to better recognition of both the object as a whole and its components as well. We will see that although the algorithms for recognising complex objects are not always trivial, in their realisation no insuperable computational complications will occur. We will realise that the recognition problems, supervised and unsupervised learning which are formulated in the statistical pattern recognition, can be, in a studied particular case, solved exactly and without additional simplifying assumptions.
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Schlesinger, M.I., Hlaváč, V. (2002). Recognition of Markovian sequences. In: Ten Lectures on Statistical and Structural Pattern Recognition. Computational Imaging and Vision, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3217-8_8
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DOI: https://doi.org/10.1007/978-94-017-3217-8_8
Publisher Name: Springer, Dordrecht
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