3D Buildings Extraction from Aerial Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00187122" target="_blank" >RIV/68407700:21230/11:00187122 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

3D Buildings Extraction from Aerial Images

Popis výsledku v původním jazyce

This paper introduces a semi-automatic method for buildings extraction through multiple-view aerial image analysis. The advantage of the used semi-automatic approach is that it allows processing of each building individually finding the parameters of buildings features extraction more precisely for each area. On the early stage the presented technique uses an extraction of line segments that is done only inside of areas specified manually. The rooftop hypothesis is used further to determine a subset ofquadrangles, which could form building roofs from a set of extracted lines and corners obtained on the previous stage. After collecting of all potential roof shapes in all images overlaps, the epipolar geometry is applied to find matching between images.This allows to make an accurate selection of building roofs removing false-positive ones and to identify their global 3D coordinates given camera internal parameters and coordinates. The last step of the image matching is based on geomet

Název v anglickém jazyce

3D Buildings Extraction from Aerial Images

Popis výsledku anglicky

This paper introduces a semi-automatic method for buildings extraction through multiple-view aerial image analysis. The advantage of the used semi-automatic approach is that it allows processing of each building individually finding the parameters of buildings features extraction more precisely for each area. On the early stage the presented technique uses an extraction of line segments that is done only inside of areas specified manually. The rooftop hypothesis is used further to determine a subset ofquadrangles, which could form building roofs from a set of extracted lines and corners obtained on the previous stage. After collecting of all potential roof shapes in all images overlaps, the epipolar geometry is applied to find matching between images.This allows to make an accurate selection of building roofs removing false-positive ones and to identify their global 3D coordinates given camera internal parameters and coordinates. The last step of the image matching is based on geomet

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/7E10045" target="_blank" >7E10045: Massive Sets of Heuristics for Machine Learning</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů