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Artificial Intelligence and Pattern Recognition, Vision, Learning

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A Guided Tour of Artificial Intelligence Research

Abstract

This chapter describes a few problems and methods combining artificial intelligence, pattern recognition, computer vision and learning. The intersection between these domains is growing and gaining importance, as illustrated in this chapter with a few examples. The first one deals with knowledge guided image understanding and structural recognition of shapes and objects in images. The second example deals with code supervision, which allows designing specific applications by exploiting existing algorithms in image processing, focusing on the formulation of processing objectives. Finally, the third example shows how different theoretical frameworks and methods for learning can be associated with the constraints inherent to the domain of robotics.

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Notes

  1. 1.

    This section is to a large part adapted from Nempont et al. (2013).

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Authors and Affiliations

  1. LTCI, Télécom ParisTech, Université Paris Saclay, Paris, France

    Isabelle Bloch

  2. GREYC, Normandie Univ., ENSICAEN, CNRS, Caen, France

    Régis Clouard & Marinette Revenu

  3. ISIR, CNRS, Sorbonne Université, Paris, France

    Olivier Sigaud

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  1. Isabelle Bloch
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  2. Régis Clouard
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  3. Marinette Revenu
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  4. Olivier Sigaud
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Correspondence to Isabelle Bloch .

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Editors and Affiliations

  1. CRIL-CNRS, Université d'Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

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Bloch, I., Clouard, R., Revenu, M., Sigaud, O. (2020). Artificial Intelligence and Pattern Recognition, Vision, Learning. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06170-8_10

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