Markov random field modeling in computer vision
Markov random field modeling in computer vision
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Prospects in Line Detection by Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
An application of mathematical morphology to road network extraction on SAR images
ISMM '98 Proceedings of the fourth international symposium on Mathematical morphology and its applications to image and signal processing
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Resolution Satellite Classification with Graph Cut Algorithms
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Power line detection from optical images
Neurocomputing
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This paper investigates the use of graph cuts for the minimization of an energy functional for road detection in satellite images, defined on the Bayesian MRF framework. The road identification process is modeled as a search for the optimal binary labeling of the nodes of a graph, representing a set of detected segments and possible connections among them. The optimal labeling corresponds to the configuration that minimizes an energy functional derived from a MRF probabilistic model, that introduces contextual knowledge about the shape of roads. We formulate an energy function modeling the interactions between road segments, while satisfying the regularity conditions required by the graph cuts based minimization. The obtained results show a noticeable improvement in terms of processing time, while achieving good results.