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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
Computer vision systems require large amounts of manually annotated
data to properly learn challenging visual concepts. Crowdsourcing
platforms offer an inexpensive method to capture human knowledge
and understanding, for a vast number of visual perception tasks.
Crowdsourcing in Computer Vision describes the types of annotations
computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while
annotation effort is minimized. It begins by discussing data
collection on both classic vision tasks, such as object
recognition, and recent vision tasks, such as visual story-telling.
It then summarizes key design decisions for creating effective data
collection interfaces and workflows, and presents strategies for
intelligently selecting the most important data instances to
annotate. It concludes with some thoughts on the future of
crowdsourcing in computer vision. Crowdsourcing in Computer Vision
provides an overview of how crowdsourcing has been used in computer
vision, enabling a computer vision researcher who has previously
not collected non-expert data to devise a data collection strategy.
It will also be of help to researchers who focus broadly on
crowdsourcing to examine how the latter has been applied in
computer vision, and to improve the methods that can be employed to
ensure the quality and expedience of data collection.
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