NDLI: Fast, Accurate and Consistent Modeling of Drainage and Surrounding Terrain

Content Provider	Springer Nature Link
Author	Fua, P.
Copyright Year	1998
Abstract	We propose an automated approach to modeling drainage channels—and, more generally, linear features that lie on the terrain—from multiple images. It produces models of the features and of the surrounding terrain that are accurate and consistent and requires only minimal human intervention.We take advantage of geometric constraints and photommetric knowledge. First, rivers flow downhill and lie at the bottom of valleys whose floors tend to be either V- or U-shaped. Second, the drainage pattern appears in gray-level images as a network of linear features that can be visually detected.Many approaches have explored individual facets of this problem. Ours unifies these elements in a common framework. We accurately model terrain and features as 3-dimensional objects from several information sources that may be in error and inconsistent with one another. This approach allows us to generate models that are faithful to sensor data, internally consistent and consistent with physical constraints. We have proposed generic models that have been applied to the specific task at hand. We show that the constraints can be expressed in a computationally effective way and, therefore, enforced while initializing the models and then fitting them to the data. Furthermore, these techniques are general enough to work on other features that are constrained by predictable forces.
Starting Page	215
Ending Page	234
Page Count	20
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	26
Issue Number	3
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1998-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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