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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
List 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.
About the author
Kristen Grauman is the Clare Boothe Luce Assistant Professor in the Department of Computer Science at the University of Texas at Austin. Her research focuses on object recognition and visual search. Before joining UT Austin in 2007, she received her Ph.D. in Computer Science from the Massachusetts Institute of Technology (2006), and a B.A. in Computer Science from Boston College (2001). Grauman has published over 40 articles in peer-reviewed journals and conferences, and work with her colleagues on large-scale visual search received the Best Student Paper Award at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in 2008. She is a Microsoft Research New Faculty Fellow, a recipient of an NSF CAREER award and the Howes Scholar Award in Computational Science, and was named one of "AI's Ten to Watch" in IEEE Intelligent Systems in 2011. She serves regularly on the program committees for the major computer vision conferences and is a member of the editorial board for theInternational Journal of Computer Vision. Bastian Leibe is an Assistant Professor at RWTH Aachen University. He holds an M.Sc. degree from Georgia Institute of Technology (1999), a Diploma degree from the University of Stuttgart (2001), and a Ph.D. from ETH Zurich (2004), all three in Computer Science. After completing his dissertation on visual object categorization at ETH Zurich, he worked as a postdoctoral research associate at TU Darmstadt and at ETH Zurich. His main research interest are in object categorization, detection, segmentation, and tracking, as well as in large-scale image retrieval and visual search. Bastian Leibe has published over 60 articles in peer-reviewed journals and conferences. Over the years, he received several awards for his research work, including the Virtual Reality Best Paper Award in 2000, the ETH Medal and the DAGM Main Prize in 2004, the CVPR Best Paper Award in 2007, the DAGM Olympus Prize in 2008, and the ICRA Best Vision Paper Award in 2009. He serves regularly on the program committee of the major computer vision conferences and is on the editorial board of the Image and Vision Computing journal.
