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Integration of Prior Knowledge Into Dense Image Matching for Video Surveillance : Volume Xl-3, Issue 1 (11/08/2014)

By Menze, M.

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Book Id: WPLBN0004014779
Format Type: PDF Article :
File Size: Pages 4
Reproduction Date: 2015

Title: Integration of Prior Knowledge Into Dense Image Matching for Video Surveillance : Volume Xl-3, Issue 1 (11/08/2014)  
Author: Menze, M.
Volume: Vol. XL-3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2014
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Heipke, C., & Menze, M. (2014). Integration of Prior Knowledge Into Dense Image Matching for Video Surveillance : Volume Xl-3, Issue 1 (11/08/2014). Retrieved from http://community.worldlibrary.net/


Description
Description: Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hanover, Germany. Three-dimensional information from dense image matching is a valuable input for a broad range of vision applications. While reliable approaches exist for dedicated stereo setups they do not easily generalize to more challenging camera configurations. In the context of video surveillance the typically large spatial extent of the region of interest and repetitive structures in the scene render the application of dense image matching a challenging task. In this paper we present an approach that derives strong prior knowledge from a planar approximation of the scene. This information is integrated into a graph-cut based image matching framework that treats the assignment of optimal disparity values as a labelling task. Introducing the planar prior heavily reduces ambiguities together with the search space and increases computational efficiency. The results provide a proof of concept of the proposed approach. It allows the reconstruction of dense point clouds in more general surveillance camera setups with wider stereo baselines.

Summary
Integration of prior knowledge into dense image matching for video surveillance

 

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