Advanced geometric camera calibration for machine vision

Vo, Minh; Wang, Zhaoyang; Luu, Long; Ma, Jun

doi:10.1117/1.3647521

Cited by 72 publications

(29 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Due to perspective projection, coupled with lens distortions, the spatial coordinates of the detected center are badly estimated. To improve the quality of the estimation, some authors have proposed a Fronto-parallel image transformation 8,14 . It involves in creating a new image that looks as if the calibration target is directly placed in front of the camera i.e is orthogonal to the optical axis of the camera.…”

Section: Improvement Using Frontal Image Approachmentioning

confidence: 99%

Intrinsic camera calibration equipped with Scheimpflug optical device

Fasogbon

Duvieubourg

Lacaze

et al. 2015

Twelfth International Conference on Quality Control by Artificial Vision 2015

View full text Add to dashboard Cite

International audienceWe present the problem of setting up an intrinsic camera calibration under Scheimpflug condition for an industrial application. We aim to calibrate the Scheimpflug camera using a roughly hand positioned calibration pattern with bundle adjustment technique. The assumptions used by classical calibration methodologies are not valid anymore for cameras undergoing Scheimpflug condition. Therefore, we slightly modify pin-hole model to estimate the Scheimpflug angles. The results are tested on real data sets captured from cameras limited by various industrial constraints, and in the presence of large distortions

show abstract

Section: Improvement Using Frontal Image Approachmentioning

confidence: 99%

Intrinsic camera calibration equipped with Scheimpflug optical device

Fasogbon

Duvieubourg

Lacaze

et al. 2015

Twelfth International Conference on Quality Control by Artificial Vision 2015

View full text Add to dashboard Cite

show abstract

“…The first step in accomplishing this was to measure the angular pointing for each camera pixel (without the sphere but with the lens) using images of a checkerboard pattern to determine lens distortion with the Camera Calibration Toolbox for MATLAB [39][40]. The toolbox routines were designed for use with visible images, but we extended their application to the thermal infrared by using a lamp as a source to apply heat to the checkerboard so that the black areas emitted more brightly than white ones.…”

Section: Angle Mappingmentioning

confidence: 99%

Reflective all-sky thermal infrared cloud imager

et al. 2018

View full text Add to dashboard Cite

Abstract:A reflective all-sky imaging system has been built using a long-wave infrared microbolometer camera and a reflective metal sphere. This compact system was developed for measuring spatial and temporal patterns of clouds and their optical depth in support of applications including Earth-space optical communications. The camera is mounted to the side of the reflective sphere to leave the zenith sky unobstructed. The resulting geometric distortion is removed through an angular map derived from a combination of checkerboardtarget imaging, geometric ray tracing, and sun-location-based alignment. A tape of highemissivity material on the side of the reflector acts as a reference that is used to estimate and remove thermal emission from the metal sphere. Once a bias that is under continuing study was removed, sky radiance measurements from the all-sky imager in the 8-14 μm wavelength range agreed to within 0.91 W/(m 2 sr) of measurements from a previously calibrated, lensbased infrared cloud imager over its 110° field of view.

show abstract

“…Waddington et al analyzed the impact of ambient light variation on measurement through the experiment 1 . Vo, Datta and Chen et al studied methods to compensate for marker extraction error during calibration [2][3][4] . Ma, and Zhang, et al did a lot of research on nonlinear response of CCD camera and DLP projector [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Error analysis of a 3D imaging system based on fringe projection technique

Zhang

Dai

2013

SPIE Proceedings

View full text Add to dashboard Cite

In the past few years, optical metrology has found numerous applications in scientific and commercial fields owing to its non-contact nature. One of the most popular methods is the measurement of 3D surface based on fringe projection techniques because of the advantages of non-contact operation, full-field and fast acquisition and automatic data processing. In surface profilometry by using digital light processing (DLP) projector, many factors affect the accuracy of 3D measurement. However, there is no research to give the complete error analysis of a 3D imaging system. This paper will analyze some possible error sources of a 3D imaging system, for example, nonlinear response of CCD camera and DLP projector, sampling error of sinusoidal fringe pattern, variation of ambient light and marker extraction during calibration. These error sources are simulated in a software environment to demonstrate their effects on measurement. The possible compensation methods are proposed to give high accurate shape data. Some experiments were conducted to evaluate the effects of these error sources on 3D shape measurement. Experimental results and performance evaluation show that these errors have great effect on measuring 3D shape and it is necessary to compensate for them for accurate measurement.

show abstract

Advanced geometric camera calibration for machine vision

Cited by 72 publications

References 9 publications

Intrinsic camera calibration equipped with Scheimpflug optical device

Intrinsic camera calibration equipped with Scheimpflug optical device

Reflective all-sky thermal infrared cloud imager

Error analysis of a 3D imaging system based on fringe projection technique

Contact Info

Product

Resources

About