A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric computation for machine vision
Geometric computation for machine vision
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust real-time ground plane motion compensation from a moving vehicle
Machine Vision and Applications
Robust and efficient map-to-image registration with line segments
Machine Vision and Applications
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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Fusion can be seen as the process of forming a uniform description of multiple information sources. The type of description depends on the application domain as well as the type of features to be considered. Features (also known as ''descriptors'') relate to iconic, symbolic, semantic or pragmatic aspects of some physical phenomenology of the real world. The process of feature extraction results in a feature map and can be top-down or bottom-up or a hybrid mixture of both. In the next step the correspondence problem (matching) of multiple feature maps has to be solved. This paper concentrates on symbolic feature extraction of straight line segments forming the feature map and their robust and precise matching with other feature maps. Together with extensions concerning point-like features these approaches form the basis of many real-time capable applications. Three of them will be described: image-to-image registration, automatic geocoding, and change detection for industrial inspection and quality assurance.