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Automatic Label Completion for Test Field Calibration : Volume Ii-5, Issue 1 (28/05/2014)

By Hieronymus, J.

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Book Id: WPLBN0003979141
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Automatic Label Completion for Test Field Calibration : Volume Ii-5, Issue 1 (28/05/2014)  
Author: Hieronymus, J.
Volume: Vol. II-5, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Hieronymus, J. (2014). Automatic Label Completion for Test Field Calibration : Volume Ii-5, Issue 1 (28/05/2014). Retrieved from http://www.hawaiilibrary.net/


Description
Description: Humboldt-Universität zu Berlin, Department of Computer Science, Computer Vision, Unter den Linden 6, 10099 Berlin, Germany. For geometric camera calibration using a test field and bundle block adjustment it is crucial to identify markers in images and label them according to a known 3D-model of the test field. The identification and labelling can become challenging, especially when the imaging system incorporates strong unknown distortion. This paper presents an algorithm, that automatically completes the labelling of unlabelled anonymous marker candidates given at least three labelled markers and a labelled 3D-model of the test field. The algorithm can be used for extracting information from images as a pre-processing step for a subsequent bundle block adjustment. It identifies an unlabelled marker candidate by referencing it to two, three or four already labelled neighbours, depending on the geometric relationship between the reference points and the candidate. This is achieved by setting up a local coordinate system, that reflects all projection properties like perspective, focal length and distortion. An unlabelled point is then represented in this local coordinate system. These local coordinates of a point are very similar in the corresponding 3D-model and in the image, which is the key idea of identifying an unlabelled point. In experiments the algorithm proofed to be robust against strong and unknown distortion, as long as the distortion does not change within a small sub-image. Furthermore no preliminary information about the focal length or exterior orientation of the camera with respect to the test field is needed.

Summary
Automatic Label Completion for Test Field Calibration

 

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