Visual object recognition
著者
書誌事項
Visual object recognition
(Synthesis lectures on artificial intelligence and machine learning, #11)
Morgan & Claypool, c2011
- : pbk
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注記
Includes bibliographical references (p. 133-162)
内容説明・目次
内容説明
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.
目次
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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