Principles of artificial intelligence
Principles of artificial intelligence
From volumes to views: an approach to 3-D object recognition
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Empirically-derived estimates of the complexity of labeling line drawings of polyhedral scenes
Artificial Intelligence
Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)
Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)
SIGMA: A Knowledge-Based Aerial Image Understanding System
SIGMA: A Knowledge-Based Aerial Image Understanding System
Model-Based Object Recognition by Geometric Hashing
ECCV '90 Proceedings of the First European Conference on Computer Vision
Remarks on the Notation of Coordinate Grammars
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Pose Estimation and Recognition of Ground Vehicles in Aerial Reconnaissance Imagery
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Probabilistic Decisions in Production Nets: An Example from Vehicle Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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A model based structural recognition approach is used for 3D detection and localization of vehicles. It is theoretically founded by syntactic pattern recognition using coordinate grammars and depicted by production nets. The computational effort significantly depends on certain tolerance parameters and the distribution of input data in the attribute domain. A brief theoretical survey of these interrelations is accompanied by comparing the performance on synthetic random data to the performance on data from different natural environments.