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Unsupervised calibration of RGB-NIR capture pairs utilizing dense multimodal image correspondences

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)978-9-0827-9701-5
ISBN (Print)978-1-5386-3736-4
Publication statusPublished - Sep 2018
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Period1/01/00 → …


In this paper, we propose an unsupervised calibration framework aimed at calibrating RGB plus Near-InfraRed (NIR) capture setups. We favour dense feature matching for the case of multimodal data and utilize the Scale-Invariant Feature Transform (SIFT) flow, previously developed for matching same-category image objects. We develop an optimization procedure that minimizes the global disparity field between the two multimodal images in order to adapt SIFT flow for our calibration needs. The proposed optimization substantially increases the number of inliers and yields more robust and unambiguous calibration results.


  • calibration, feature extraction, image colour analysis, image matching, optimisation, transforms, SIFT flow, unambiguous calibration results, optimization procedure, same-category image objects, multimodal data, dense feature matching, unsupervised calibration framework, dense multimodal image correspondences, RGB-NIR capture pairs, global disparity field minimization, RGB-plus-near-infrared capture setups, scale-invariant feature transform flow, Cameras, Calibration, Feature extraction, Matched filters, Optimization, Sensors, Genetic algorithms, NIR, multimodal stereo, features matching

Publication forum classification

Field of science, Statistics Finland