000183304 001__ 183304
000183304 005__ 20180913061725.0
000183304 037__ $$aCONF
000183304 245__ $$aPose estimation of landscape images using DEM and orthophotos
000183304 269__ $$a2012
000183304 260__ $$c2012
000183304 336__ $$aConference Papers
000183304 520__ $$aIn this paper, we propose a methodology for the estimation of the pose of oblique landscape images. Knowledge about the pose is needed for using such images in augmented reality applications or to allow projection of pixels in a GIS for spatial analysis. We propose to estimate the pose using a 3D digital elevation model (DEM) rendered with an ortho-image as reference. Starting from a rough estimation, the pose is refined by exploiting correspondences detected with a local normalized cross-correlation method. Matches are searched between edge features extracted both in the query image and in a synthetic image generated from the DEM and the ortho-image. A RANSAC approach based on the camera model extracts the best matches. Few iterations of the algorithm provide a precise estimation of the pose, leading to a precise georeferencing of the query image. We tested the proposed methodology to images of a popular glacier in the south of Switzerland downloaded from Panoramio.
000183304 6531_ $$apose of oblique landscape images
000183304 6531_ $$aOrtho-image
000183304 6531_ $$aRANSAC approach
000183304 700__ $$0242397$$g167401$$aProduit, Timothée
000183304 700__ $$0245927$$g150680$$aTuia, Devis
000183304 700__ $$g105275$$aGolay, François$$0242392
000183304 700__ $$aStrecha, Christoph$$g182325$$0244088
000183304 7112_ $$dChina$$cXiamen$$aInt. Conference on Computer Vision in Remote Sensing CVRS
000183304 909C0 $$xU10244$$0252045$$pLASIG
000183304 909C0 $$pCVLAB$$xU10659$$0252087
000183304 909CO $$pconf$$pIC$$pENAC$$ooai:infoscience.tind.io:183304
000183304 917Z8 $$x149002
000183304 937__ $$aEPFL-CONF-183304
000183304 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000183304 980__ $$aCONF