
Visual Object Recognition by Kristen Grauman
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.-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
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.
| SKU | Unavailable |
| ISBN 13 | 9783031004254 |
| ISBN 10 | 3031004256 |
| Title | Visual Object Recognition |
| Author | Kristen Grauman |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2011-04-19 |
| Number of pages | 163 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |












































