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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010093313 | Q325.5 M34 2005 | Open Access Book | Book | Searching... |
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Summary
Summary
This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.
Reviews 1
Choice Review
Efficient learning and (visual) perception algorithms are crucial to autonomous mobile robotic artifacts. While in a given environment, they learn from it and respond to its stimuli. The unifying niche of this volume's eight chapters is the use of state-of-the-art computational techniques in enhancing robotic vision. All chapters present successful case studies on applying their proposed models. The topics span a wide range of problems and suggested solutions. General-purpose deformable model-based object detection systems are treated in the first chapter, whereas the second focuses on vision implementations of space-invariant images. Chapters 3 and 4 propose wavelet and reinforcement learning algorithms; chapter 5 emphasizes an optical-based motion estimation curve evolution model. Chapter 6 presents a 3-D model for real-world objects using range and intensity images, and chapter 7 uses a 3-D human body model for analyses of human motion. The last chapter focuses on user modeling with dialogue strategies. This volume gives a different perspective on many problems and useful case studies, thus making it a valuable scholarly compilation. ^BSumming Up: Recommended. Upper-division undergraduates through faculty. G. Trajkovski Towson University
Table of Contents
| 1 Learning Visual Landmarks for Mobile Robot Topological Navigation | p. 1 |
| 2 Foveated Vision Sensor and Image Processing - A Review | p. 57 |
| 3 On-line Model Learning for Mobile Manipulations | p. 99 |
| 4 Continuous Reinforcement Learning Algorithm for Skills Learning in an Autonomous Mobile Robot | p. 137 |
| 5 Efficient Incorporation of Optical Flow into Visual Motion Estimation in Tracking | p. 167 |
| 6 3-D Modeling of Real-World Objects Using Range and Intensity Images | p. 203 |
| 7 Perception for Human Motion Understanding | p. 265 |
| 8 Cognitive User Modeling Computed by a Proposed Dialogue Strategy Based on an Inductive Game Theory | p. 325 |
