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A Graph-Based Representation to Detect Linear Features

机译:用于检测线性特征的基于图的表示形式

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摘要

Graph-based representations of the scene are well adapted to introduce high-level knowledge in image segmentation. The problem consists then in searching the graph configuration or labeling minimizing some cost function. In the case of local relationships between the graph nodes, the Markovian frame- work and simulated annealing algorithms provide some answers to this question. We are interested in this paper in the automatic or semi-automatic detection of linear structures like Roads or hydrological networks in satellite radar images. Using a graph of segments and introducing Local contextual properties of these networks, a Markov Random Field is defined to perform the Detection. Interaction choice relies on a priori knowledge on the usual aspect of the linear objects to Detect. Results are presented for real radar images both for road and hydrological networks.
机译:基于图形的场景表示非常适合在图像分割中引入高级知识。然后,问题在于搜索图形配置或标记以最小化某些成本函数。对于图节点之间的局部关系,马尔可夫框架和模拟退火算法为该问题提供了一些答案。我们对本文感兴趣的是自动或半自动检测卫星雷达图像中的道路或水文网络等线性结构。使用段图并引入这些网络的本地上下文属性,定义了马尔可夫随机字段来执行检测。交互选择依赖于要检测的线性对象通常方面的先验知识。给出了道路和水文网络实际雷达图像的结果。

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