Multisensor Fusion for Computer Vision

Fusion or integration of information from multiple sensors allows a computer to achieve the optimum interpretation of a visual scene. This NATO workshop volume presents current research in the field by the leading research groups in Europe and North America.

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This volume presents a set of papers based on theproceedings of the NATO Advanced Research Workshop onMultisensor Fusion for Computer Vision, held in Grenoble,France, in June 1989. The workshop focused on the fusion orintegration of sensor information to achieve the optimuminterpretation of a scene. The papers cover a broad range oftopics, including principles and issues in multisensorfusion, information fusion for navigation, multisensorfusion for object recognition, network approaches tomultisensor fusion, computer architectures for multisensorfusion, and applications of multisensor fusion.The authors have documented their own research and, in sodoing,have presented the state of the art in the field.Each author is a recognized leader in his or her area in theacademic, governmental, or industrial research community.Several contributors present novel points of view on theintegration of information. The book gives a representativepicture of current progress in multisensor fusion forcomputer vision among the leading research groups in Europeand North America.

Contenu
Information Integration and Model Selection in Computer Vision.- Principles and Techniques for Sensor Data Fusion.- The Issues, Analysis, and Interpretation of Multisensor Images.- Physically-Based Fusion of Visual Data over Space, Time, and Scale.- What Can Be Fused?.- Kalman Filter-Based Algorithms for Estimating Depth from Image Sequences.- Robust Linear Rules for Nonlinear Systems.- Geometric Sensor Fusion in Robotics (Abstract).- Cooperation between 3D Motion Estimation and Token Trackers (Abstract).- Three-Dimensional Fusion from a Monocular Sequence of Images.- Fusion of Range and Intensity Image Data for Recognition of 3D Object Surfaces.- Integrating Driving Model and Depth for Identification of Partially Occluded 3D Models.- Fusion of Color and Geometric Information.- Evidence Fusion Using Constraint Satisfaction Networks.- Multisensor Information Integration for Object Identification.- Distributing Inferential Activity for Synchronic and Diachronic Data Fusion.- Real-Time Perception Architectures: The SKIDS Project.- Algorithms on a SIMD Processor Array.- Shape and Curvature Data Fusion by Conductivity Analysis (Abstract).- A Knowledge-Based Sensor Fusion Editor.- Multisensor Change Detection for Surveillance Applications.- Multisensor Techniques for Space Robotics.- Coordinated Use of Multiple Sensors in a Robotic Workcell.- Neural Network Based Inspection of Machined Surfaces Using Multiple Sensors.- Adaptive Visual/Auditory Fusion in the Target Localization System of the Barn Owl.- Index of Key Terms.- Workshop Speakers.