找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Automatic Extraction of Man-Made Objects from Aerial and Space Images (II); Armin Gruen,Emmanuel P. Baltsavias,Olof Henricsson Conference

[复制链接]
楼主: 磨损
发表于 2025-3-25 04:27:26 | 显示全部楼层
A model driven approach to extract buildings from multi-view aerial imageryposed approach combines bottom-up and topdown processing. In this paper the emphasis is on the discussion of the experimental evaluation. To evaluate statistically the performance of the system, a set of 100 realisations of 5 images from different viewpoints was used, which was generated by combinin
发表于 2025-3-25 08:07:51 | 显示全部楼层
发表于 2025-3-25 14:15:42 | 显示全部楼层
发表于 2025-3-25 16:02:40 | 显示全部楼层
On the Integration of Object Modeling and Image Modeling in Automated Building Extraction from Aeriahased on the recognition of simple building components and the successive aggregation of these building components to complete building descriptions, thereby analyzing 2D information as well as 3D information. This paper emphasizes that the modeling of the projective appearances of buildings and bui
发表于 2025-3-25 23:36:43 | 显示全部楼层
TOBAGO — a topology builder for the automated generation of building modelsst the operator to measure the house roofs from a stereomodel in form of an unstructured point cloud. According to our experience this can be done very quickly. In a second step we fit generic building models fully automatically to these point clouds. The structure information is inherently included
发表于 2025-3-26 02:34:07 | 显示全部楼层
Crestlines contribution to the automatic building extractioncontext of Mobile Communication Network Planning, our interest focuses on an input dataset including a stereo pair of aerial images and a DSM (Digital Surface Model) modeling all 3D objects. DSM is provided either by stereo-vision or by active sensors. In this paper, we propose to use crestlines ext
发表于 2025-3-26 08:13:27 | 显示全部楼层
Recognizing Buildings in Aerial Imagesraphs. Depending on the level of matching, the given picture is classified as building or background. The graphs are constructed based on a learning set and using an entropy criterion to separate building images and background images by recursive partitioning. In the future we hope to extend our alg
发表于 2025-3-26 09:47:12 | 显示全部楼层
发表于 2025-3-26 13:01:01 | 显示全部楼层
发表于 2025-3-26 19:16:12 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-3 19:31
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表