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Demonstrates state-of-the-art in applying computer vision in robotics
Discusses contrasting viewpoints of computer vision vs. robotics
Provides current and future challenges discussed from a research perspective
Covers vision considered as a component of an action generating intelligent system
The field of computer vision has developed significantly over the last several years and the application areas span a broad range from medicine to robotics and surveillance.
Unifying Perspectives in Computational and Robot Vision provides an overview of the latest research surrounding vision algorithms and their use and evaluation with robotic applications.
In robotics, computer vision is mainly seen as another sensory modality and new algorithms are commonly developed for different applications. In the computer vision community, there have been more requirements for benchmarking and comparison of methods but very few are still applied in realistic settings and in real-time which is one of the strongest requirements in robotic applications. Unifying Perspectives in Computational and Robot Vision brings the communities of robot and computer vision to a joint appreciation of the value of systems, where there is a need to consider all aspects from perception to action generation.
Based on a workshop "From Features to Actions: Unifying Perspectives in Computational and Robot Vision" that was organized by the editors at the IEEE International Conference on Robotics and Automation (Rome, April 2007), Unifying Perspectives in Computational and Robot Vision will be a valuable resource for readers.
Recent Trends in Computational and Robot Vision.- Extracting Planar Kinematic Models Using Interactive Perception.- People Detection Using Multiple Sensors on a Mobile Robot.- Perceiving Objects and Movements to Generate Actions on a Humanoid Robot.- Wald's Sequential Analysis for Time-constrained Vision Problems.- Pose Estimation and Feature Tracking for Robot Assisted Surgery with Medical Imaging.- A Sliding Window Filter for Incremental SLAM.- Topological and Metric Robot Localization through Computer Vision Techniques.- More Vision for SLAM.- Maps, Objects and Contexts for Robots.- Vision-Based Navigation Strategies.- Image-Based Visual Servoing with Extra Task Related Constraints in a General Framework for Sensor-Based Robot Systems.