Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the range of applicability of an artificial intelligence machine
Parallel computation and computers for artificial intelligence
Integrated Analysis of Thermal and Visual Images for Scene Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visible surface reconstruction via local minimax approximation
Pattern Recognition
Evidence-Based Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Structuring and Constraint Satisfaction: The Mapsee Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation using laser radar data
Pattern Recognition
An experimental target recognition system for laser radar imagery
Proceedings of a workshop on Image understanding workshop
The interpretation of laser radar images by a knowledge-based system
Machine Vision and Applications
Image Segmentation by Unifying Region and Boundary Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Target Recognition in Pulsed Ladar Imagery
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Unsupervised Texture Segmentation Using Stochastic Version of the EM Algorithm and Data Fusion
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Segmentation and description of natural outdoor scenes
Image and Vision Computing
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The AIMS (automatic interpretation using multiple sensors) system, which uses registered laser radar and thermal imagers, is discussed. Its objective is to detect and recognize man-made objects at kilometer range in outdoor scenes. The multisensor fusion approach is applied to four sensing modalities (range, intensity, velocity, and thermal) to improve both image segmentation and interpretation. Low-level attributes of image segments (regions) are computed by the segmentation modules and then converted to the KEE format. The knowledge-based interpretation modules are constructed using KEE and Lisp. AIMS applies forward chaining in a bottom-up fashion to derive object-level interpretations from databases generated by the low-level processing modules. The efficiency of the interpretaton process is enhanced by transferring nonsymbolic processing tasks to a concurrent service manager (program). A parallel implementation of the interpretation module is reported. Experimental results using real data are presented.