NDLI: Recognizing cars in aerial imagery to improve orthophotos

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Recognizing cars in aerial imagery to improve orthophotos

Content Provider	ACM Digital Library
Author	Leberl, Franz Bischof, Horst Kluckner, Stefan Grabner, Helmut
Abstract	The automatic creation of 3D models of urban spaces has become a very active field of research. This has been inspired by recent applications in the location-awareness on the Internet, as demonstrated in maps.live.com and similar websites. The level of automation in creating 3D city models has increased considerably, and has benefited from an increase in the redundancy of the source imagery, namely digital aerial photography. In this paper we argue that the next big step forward is to replace photographic texture by an interpretation of what the texture describes, and to achieve this fully automatically. One calls the result "semantic knowledge". For example we want to know that a certain part of the image is a car, a person, a building, a tree, a shrub, a window, a door, instead of just a collection of 3D points or triangles with a superimposed photographic texture. We investigate object recognition methods to make this next big step. We demonstrate an early result of using the on-line variant of a Boosting algorithm to indeed detect cars in aerial digital imagery to a satisfactory and useful level of completeness. And we show that we can use this semantic knowledge to produce improved orthophotos. We expect that also the 3D models will be improved by the knowledge of cars.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781595939142
DOI	10.1145/1341012.1341015
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic information Aerial images 3d city models Boosting Object recognition Online learning Car detection
Content Type	Text
Resource Type	Article

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