Measurement of mirror surfaces using specular reflection and analytical computation

Wang, Zhen Zhou; Huang, Xinyu; Yang, Ruigang; Zhang, Yuming

doi:10.1007/s00138-012-0432-6

Cited by 15 publications

(33 citation statements)

References 4 publications

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“…In most cases [11][12][13][14][15][16][17][18][19][20], the designed patterns keep the distances between adjacent markers the same for convenience. In this section, we propose a 3D pattern modeling method to eliminate the noise based on the distances between the center marker and the other markers.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…This paper mainly deals with the noise of projector calibration and camera calibration in the structured light scanning system [11][12][13][14][15][16][17][18][19]. The structured light scanning methods [11][12][13][14][15][16][17][18][19] project some designed patterns onto the surface of the object to be measured. From the distorted pattern, the surface's profile could be calculated based on the system parameters calculated from a set of calibrated markers.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a method to eliminate both the noise and radial distortion during calibration for a structured light system [12] which uses a Pico laser projector to generate the structured light pattern that suffers both radial distortion and noise. Consequently, the measurement accuracy is very poor.…”

Section: Introductionmentioning

confidence: 99%

“…The radial distortion is avoided and the contrast of the image is also increased because of the higher power of SNF laser compared to the Pico laser projector. Hence, the measurement accuracy is increased over 10 order of magnitude compared to that of [12]. To increase the measurement accuracy of the structured light system with a projector [12], we model the markers in the perfectly designed pattern with an arbitrarily assigned center as rays.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the measurement accuracy is increased over 10 order of magnitude compared to that of [12]. To increase the measurement accuracy of the structured light system with a projector [12], we model the markers in the perfectly designed pattern with an arbitrarily assigned center as rays. We then search for an optimal plane that intercepts the rays and produces an optimal pattern that is closest to the actually captured pattern.…”

Section: Introductionmentioning

confidence: 99%

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Removal of noise and radial lens distortion during calibration of computer vision systems

Wang

2015

Opt. Express

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Abstract:The calibration of computer vision systems that contain the camera and the projector usually utilizes markers of the well-designed patterns to calculate the system parameters. Undesirably, the noise and radial distortion exist universally, which decreases the calibration accuracy and consequently decreases the measurement accuracy of the related technology. In this paper, a method is proposed to remove the noise and radial distortion by registering the captured pattern with an ideal pattern. After the optimal modeled pattern is obtained by registration, the degree of freedom of the total calibration markers is reduced to one and both the noise and radial distortion are removed successfully. The accuracy improvement in a structured light scanning system is over 10 24 order of magnitude in the sense of mean square errors. Most importantly, the proposed method can be readily adopted by the computer vision techniques that use projectors or cameras.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Removal of noise and radial lens distortion during calibration of computer vision systems

Wang

2015

Opt. Express

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Detection and localization of specular surfaces using image motion cues

Yılmaz

Doerschner

2014

Machine Vision and Applications

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Successful identification of specularities in an image can be crucial for an artificial vision system when extracting the semantic content of an image or while interacting with the environment. We developed an algorithm that relies on scale and rotation invariant feature extraction techniques and uses motion cues to detect and localize specular surfaces. Appearance change in feature vectors is used to quantify the appearance distortion on specular surfaces, which has previously been shown to be a powerful indicator for specularity (Doerschner et al. in Curr Biol, 2011). The algorithm combines epipolar deviations (Swaminathan et al. in Lect Notes Comput Sci 2350:508-523, 2002) and appearance distortion, and succeeds in localizing specular objects in computer-rendered and real scenes, across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions. © 2014 Springer-Verlag Berlin Heidelberg

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An LSTM-based measurement method of 3D weld pool surface in GTAW

Cheng

2021

Measurement

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Measurement of mirror surfaces using specular reflection and analytical computation

Cited by 15 publications

References 4 publications

Removal of noise and radial lens distortion during calibration of computer vision systems

Removal of noise and radial lens distortion during calibration of computer vision systems

Detection and localization of specular surfaces using image motion cues

An LSTM-based measurement method of 3D weld pool surface in GTAW

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