Extended Phase Field Higher-Order Active Contour Models for Networks
文献类型：期刊论文
| 作者 | Peng, Ting1,2,3; Jermyn, Ian H.3; Prinet, Veronique1,2; Zerubia, Josiane3 |
| 刊名 | INTERNATIONAL JOURNAL OF COMPUTER VISION
 |
| 出版日期 | 2010-05-01 |
| 卷号 | 88期号:1页码:111-128 |
| 关键词 | Active contour Phase field Shape prior Parameter analysis Remote sensing Road network extraction |
| 英文摘要 | 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 new phase field higher-order active contour (HOAC) prior models for network regions, and apply them 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 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. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | ROAD EXTRACTION ; AERIAL IMAGES ; SHAPE ; SEGMENTATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000275753900006 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9992]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | 1.CASIA, LIAMA, Beijing 100190, Peoples R China 2.CASIA, NLPR, Beijing 100190, Peoples R China 3.INRIA, Project Team Ariana, F-06902 Sophia Antipolis, France |
| 推荐引用方式 GB/T 7714 | Peng, Ting,Jermyn, Ian H.,Prinet, Veronique,et al. Extended Phase Field Higher-Order Active Contour Models for Networks[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2010,88(1):111-128. |
| APA | Peng, Ting,Jermyn, Ian H.,Prinet, Veronique,&Zerubia, Josiane.(2010).Extended Phase Field Higher-Order Active Contour Models for Networks.INTERNATIONAL JOURNAL OF COMPUTER VISION,88(1),111-128. |
| MLA | Peng, Ting,et al."Extended Phase Field Higher-Order Active Contour Models for Networks".INTERNATIONAL JOURNAL OF COMPUTER VISION 88.1(2010):111-128. |
入库方式： OAI收割
来源：自动化研究所
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