Visual object recognition

Author(s)

• Grauman, Kristen Lorraine
• Leibe, Bastian

Bibliographic Information

Visual object recognition

Kristen Grauman, Bastian Leibe

（Synthesis lectures on artificial intelligence and machine learning, #11）

Morgan & Claypool, c2011

• : pbk

Note

Includes bibliographical references (p. 133-162)

Description and Table of Contents

Description

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

by "Nielsen BookData"

Details

• NCID: BB06576982
• ISBN:
  • 9781598299687
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: [San Rafael, Calif.]
• Pages/Volumes: xvii, 163 p.
• Size: 24 cm
• Parent Bibliography ID:
  • BA85080963