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
General
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