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Corner-based geometric calibration of multi-focus plenoptic cameras

Research output: Contribution to journalConference paper

Sotiris Nousias, Francois Chadebecq, Jonas Pichat, Pearse Keane, Sebastien Ourselin, Christos Bergeles

Original languageEnglish
Pages (from-to)957-965
Number of pages9
Journal2017 IEEE International Conference on Computer Vision (ICCV)
Volume2017-October
DOIs
Publication statusPublished - 25 Dec 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

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  • Corner-Based Geometric Calibration_NOUSIAS_Published25December2018_GREEN AAM

    Corner_Based_Geometric_Calibration_NOUSIAS_Published25December2018_GREEN_AAM.pdf, 6.1 MB, application/pdf

    23/01/2019

    Accepted author manuscript

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Abstract

We propose a method for geometric calibration of multifocus plenoptic cameras using raw images. Multi-focus plenoptic cameras feature several types of micro-lenses spatially aligned in front of the camera sensor to generate micro-images at different magnifications. This multi-lens arrangement provides computational-photography benefits but complicates calibration. Our methodology achieves the detection of the type of micro-lenses, the retrieval of their spatial arrangement, and the estimation of intrinsic and extrinsic camera parameters therefore fully characterising this specialised camera class. Motivated from classic pinhole camera calibration, our algorithm operates on a checker-board's corners, retrieved by a custom microimage corner detector. This approach enables the introduction of a reprojection error that is used in a minimisation framework. Our algorithm compares favourably to the state-of-the-art, as demonstrated by controlled and freehand experiments, making it a first step towards accurate 3D reconstruction and Structure-from-Motion.

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