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A Guided Tour of Artificial Intelligence ResearchArtificial Intelligence and Pattern Recognition, Vision, Learning

A Guided Tour of Artificial Intelligence Research: Artificial Intelligence and Pattern... [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guided Tour of Artificial Intelligence ResearchArtificial Intelligence and Pattern Recognition, Vision, Learning

Editors: Marquis, Pierre; Papini, Odile; Prade, Henri
Bloch, Isabelle; Clouard, Régis; Revenu, Marinette; Sigaud, Olivier
A Guided Tour of Artificial Intelligence Research — May 8, 2020
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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-06169-2
Pages
337 –364
DOI
10.1007/978-3-030-06170-8_10
Publisher site
See Chapter on Publisher Site

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.]

Published: May 8, 2020

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