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Publications about Dense urban area
Result of the query in the list of publications :
Article |
1 - A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models..
M. Ortner and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 2008.
Keywords : Image procressing, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register.
Copyright :
@ARTICLE{ortner08,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.}, |
| year |
= |
{2008}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/27/88/82/PDF/ortner08.pdf}, |
| keyword |
= |
{Image procressing, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register} |
| } |
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Conference article |
1 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. European Conference on Computer Vision (ECCV), Marseille, France, October 2008.
Keywords : Dense urban area, Phase Field, Road network, Variational methods, Very high resolution.
Copyright :
@INPROCEEDINGS{Peng08c,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images}, |
| year |
= |
{2008}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
| address |
= |
{Marseille, France}, |
| pdf |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-88690-7_38}, |
| keyword |
= |
{Dense urban area, Phase Field, Road network, Variational methods, Very high resolution} |
| } |
Abstract :
This paper addresses the segmentation from an image of entities that have the form of a 'network', i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present a new phase field higher-order active contour (HOAC) prior model for network regions, and apply it to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much 'noise' in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a severe limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. To solve this problem, we propose a new HOAC prior energy term, and reformulate it as a nonlocal phase field energy. We analyse the stability of the new model, and find that in addition to solving the above problem by separating the interactions between points on the same and opposite sides of a network branch, the new model permits the modelling of two widths
simultaneously. The analysis also fixes some of the model parameters in terms of network width(s). After adding a likelihood energy, we use the model to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The results demonstrate the superiority of the new model, the importance of strong prior knowledge in general, and of the new term in particular. |
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