Multisensor Fusion for Computer Vision

✍ Scribed by Jan-Olof Eklundh (auth.), Dr. J. K. Aggarwal (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 1993
• Tongue: English
• Leaves: 449
• Series: NATO ASI Series 99
• Edition: 1
• Category: Library

✦ Synopsis

This volume contains revised papers based on contributions to the NATO Advanced Research Workshop on Multisensor Fusion for Computer Vision, held in Grenoble, France, in June 1989. The 24 papers presented here cover a broad range of topics, including the principles and issues in multisensor fusion, information fusion for navigation, multisensor fusion for object recognition, network approaches to multisensor fusion, computer architectures for multi sensor fusion, and applications of multisensor fusion. The participants met in the beautiful surroundings of Mont Belledonne in Grenoble to discuss their current work in a setting conducive to interaction and the exchange of ideas. Each participant is a recognized leader in his or her area in the academic, governmental, or industrial research community. The workshop focused on techniques for the fusion or integration of sensor information to achieve the optimum interpretation of a scene. Several participants presented novel points of view on the integration of information. The 24 papers presented in this volume are based on those collected by the editor after the workshop, and reflect various aspects of our discussions. The papers are organized into five parts, as follows.

✦ Table of Contents

Front Matter....Pages I-X
Front Matter....Pages 1-1
Information Integration and Model Selection in Computer Vision....Pages 3-13
Principles and Techniques for Sensor Data Fusion....Pages 15-36
The Issues, Analysis, and Interpretation of Multi-Sensor Images....Pages 37-62
Physically-Based Fusion of Visual Data over Space, Time, and Scale....Pages 63-69
What Can be Fused?....Pages 71-84
Front Matter....Pages 85-85
Kalman Filter-based Algorithms for Estimating Depth from Image Sequences....Pages 87-130
Robust Linear Rules for Nonlinear Systems....Pages 131-150
Geometric Sensor Fusion in Robotics....Pages 151-151
Cooperation between 3D Motion Estimation and Token Trackers....Pages 153-153
Three-Dimensional Fusion from a Monocular Sequence of Images....Pages 155-167
Front Matter....Pages 169-169
Fusion of Range and Intensity Image Data for Recognition of 3D object surfaces....Pages 171-194
Integrating Driving Model and Depth for Identification of Partially Occluded 3D Models....Pages 195-211
Fusion of Color and Geometric Information....Pages 213-237
Evidence Fusion Using Constraint Satisfaction Networks....Pages 239-253
Multisensor Information Integration for Object Identification....Pages 255-276
Front Matter....Pages 277-277
Distributing Inferential Activity for Synchronic and Diachronic Data Fusion....Pages 279-291
Real-Time Perception Architectures: The SKIDS Project....Pages 293-305
Algorithms on a SIMD processor array....Pages 307-322
Shape and Curvature Data Fusion by Conductivity Analysis....Pages 323-323
A Knowledge Based Sensor Fusion Editor....Pages 325-341
Front Matter....Pages 343-343
Multisensor Change Detection for Surveillance Applications....Pages 345-365
Multisensor Techniques for Space Robotics....Pages 367-393
Coordinated Use of Multiple Sensors in a Robotic Workcell....Pages 395-420
Neural Network Based Inspection of Machined Surfaces Using Multiple Sensors....Pages 421-437
Adaptive Visual/Auditory Fusion in the Target Localization System of the Barn Owl....Pages 439-450
Back Matter....Pages 451-460

✦ Subjects

Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition