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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Stereo Vision Auto-Alignment And
The Unsupervised Search For Objects Of Interest With Depth Estimation |
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Authors: |
Ling-Wei
Lee, Faeznor Diana binti Zainordin |
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Published in:
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(2013).ECMS 2013 Proceedings edited
by: W. Rekdalsbakken, R. T. Bye, H.
Zhang European Council for Modeling and Simulation. doi:10.7148/2013 ISBN:
978-0-9564944-6-7 27th
European Conference on Modelling and Simulation, Aalesund,
Norway, May 27th – 30th,
2013 |
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Citation
format: |
Ling-Wei Lee, Faeznor Diana
binti Zainordin (2013). Stereo Vision Auto-Alignment And The Unsupervised
Search For Objects Of Interest With Depth Estimation,
ECMS 2013 Proceedings edited by: W. Rekdalsbakken, R. T. Bye, H. Zhang, European Council for Modeling
and Simulation. doi:10.7148/2013-0817 |
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DOI: |
http://dx.doi.org/10.7148/2013-0817 |
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Abstract: |
Stereo
vision
is fast becoming a highly investigated area in the domain of image
processing. Depth information may be obtained from stereo or multi-vision
images for reconstructing objects in 3D based on 2D information. Robotic
applications make use of stereo vision for navigation
purposes, locking down targets, as well as simulating human-like behaviour.
This paper presents an algorithm for the auto-alignment of stereo images followed
by the self-extraction of objects of interest using an unsupervised search.
Based on the understanding that different objects or regions are focused at
different focal points, alignment between the two images is carried out to
determine areas of high overlapping similarities. Objects are then identified
in these selected areas with their estimated depth calculated based on the
disparities between the stereo images. Results obtained for tests carried out
on several experimental image pairs showed good extraction of the objects
with close-to-real-world values obtained for the distances of the objects
from the cameras. |
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