EAN: 9783031004254

SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE A...

The visual recognition problem is central to computer vision research. From robotics to
information retrieval  many desired applications demand the ability to identify and localize
categories  places  and objects. This tutorial overviews computer vision algorithms for visual
object recognition and image classification. We introduce primary representations and learning
approaches  with an emphasis on recent advances in the field. The target audience consists of
researchers or students working in AI  robotics  or vision who would like to understand what
methods and representations are available for these problems. This lecture summarizes what is
and isn't possible to do reliably today  and overviews key concepts that could be employed in
systems requiring visual categorization. Table of Contents: Introduction   Overview:
Recognition of Specific Objects   Local Features: Detection and Description   Matching Local
Features   Geometric Verification of Matched Features   Example Systems: Specific-Object
Recognition   Overview: Recognition of Generic Object Categories   Representations for Object
Categories   Generic Object Detection: Finding and Scoring Candidates   Learning Generic Object
Category Models   Example Systems: Generic Object Recognition   Other Considerations and
Current Challenges   Conclusions

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