The effects of temperature variation on videometric measurement and a compensation method

Yu, Qifeng; Chao, Zhichao; Jiang, Guangwen; Shang, Yang; Fu, Shaojing; Liu, Xiaolin; Zhu, Xianwei; Liu, Haibo

doi:10.1016/j.imavis.2014.08.011

Cited by 25 publications

(38 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 4 confirms the changes in the principal distance and the principal point resulting in directional zooming effects. Similar observations were made by [14]. Focussing on the different camera types, the principle point of the LG Google Nexus 5 camera moves to the lower right, whereas the principle point of the Samsung Galaxy S8 camera moves to the upper left.…”

Section: Self-heating Temperature Impacts At Smartphone Camerassupporting

confidence: 71%

“…As can be taken from the literature, correlations are mainly found between the depth direction and the principal distance t z ⇔ c as well as the principal point and the EOP x 0 , y 0 ⇔ t x , t y , t z , ω, φ, κ [33,35]. Exemplary for this, [14] point out in their study on temperature-related image shifts that the principle point and the translation parameters t x , t y show similar motion patterns, which is mainly related to correlations between the involved parameters. For that reasons, one should be cautious with conclusions about the origin of camera effects in interpreting changes of the camera model.…”

Section: Iop Estimationmentioning

confidence: 99%

“…Alternating IOP can be caused by aperture-or focus adjustment on the one hand or due to physical impacts such as strong motion on the other hand, e.g., [12,13]. In addition, [14][15][16][17] have shown that ambient temperature changes greatly influence the IOP of webcams, digital single-lens reflex (DSLR) cameras and bridge cameras resulting in image shifts and zooming effects. However, there is still a knowledge gap regarding the relationship between changing temperatures and the interior geometry of low-cost cameras based on the micro-electro-mechanical system (MEMS) technology built in smartphones or used as RPi cameras.…”

Section: Introductionmentioning

confidence: 99%

“…These errors range from a few millimetres to several centimetres depending on the camera-to-object distance. Apart from measurement applications provided for environmental monitoring, temperature-induced variations of the camera geometry are considerable issues in, e.g., machine vision [14,15], automotive [19] or medicine [20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Elias

Eltner

Liebold

et al. 2020

Sensors

View full text Add to dashboard Cite

Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera’s geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y-direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera’s depth direction.

show abstract

Section: Self-heating Temperature Impacts At Smartphone Camerassupporting

confidence: 71%

Section: Iop Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Elias

Eltner

Liebold

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…More recently, the pattern of thermal induced image shifts has been modelled and incorporated into the camera calibration approach (Podbreznik and Potocnik, 2011;Yu et al, 2014). …”

Section: Camera Warm-up Effectsmentioning

confidence: 99%

Close Range Calibration of Long Focal Length Lenses in a Changing Environment

Robson¹,

MacDonald²,

Kyle³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:University College London is currently developing a large-scale multi-camera system for dimensional control tasks in manufacturing, including part machining, assembly and tracking, as part of the Light Controlled Factory project funded by the UK Engineering and Physical Science Research Council. In parallel, as part of the EU LUMINAR project funded by the European Association of National Metrology Institutes, refraction models of the atmosphere in factory environments are being developed with the intent of modelling and eliminating the effects of temperature and other variations. The accuracy requirements for both projects are extremely demanding, so accordingly improvements in the modelling of both camera imaging and the measurement environment are essential. At the junction of these two projects lies close range camera calibration. The accurate and reliable calibration of cameras across a realistic range of atmospheric conditions in the factory environment is vital in order to eliminate systematic errors. This paper demonstrates the challenge of experimentally isolating environmental effects at the level of a few tens of microns. Longer lines of sight promote the use and calibration of a near perfect perspective projection from a Kern 75mm lens with maximum radial distortion of the order of 0.5m. Coordination of a reference target array, representing a manufactured part, is achieved to better than 0.1mm at a standoff of 8m. More widely, results contribute to better sensor understanding, improved mathematical modelling of factory environments and more reliable coordination of targets to 0.1mm and better over large volumes.

show abstract

Mitigating thermal‐induced image drift for videogrammetric technique in support of structural monitoring applications

Zhou

et al. 2021

Structural Contr & Hlth

View full text Add to dashboard Cite

The thermal effect that leads to spurious image drift is one major concern in the videogrammetric measurement. On the ground that the image drift originates from the thermal-induced displacement of the image sensor board, this study proposes to establish the thermal-induced displacement function of the image plane for the prediction of image drift and the elimination thereof. The displacement function of the image plane is calibrated with stationary targets easily available in laboratory. In measurements other than the thermal effect calibration, the thermal-induced displacement of the image plane is predicted from the precalibrated displacement function. It is then subtracted from the measured pixel coordinates of measurand, resulting in normalized pixel coordinates in which the thermal-induced image drift has been removed. To exemplify this approach, two thermal effect calibration tests with different camera orientations are fulfilled. The displacement function of the image plane is simulated with a first-order polynomial model. Verification tests are performed in both temperature-controlled and outdoor environments. The approach achieves satisfactory performance in both environments.

show abstract

The effects of temperature variation on videometric measurement and a compensation method

Cited by 25 publications

References 13 publications

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Close Range Calibration of Long Focal Length Lenses in a Changing Environment

Mitigating thermal‐induced image drift for videogrammetric technique in support of structural monitoring applications

Contact Info

Product

Resources

About