Statistical behavior analysis and precision optimization for the laser stripe center detector based on Steger's algorithm

Qi, Li; Zhang, Yixin; Zhang, Xuping; Wang, Shun; Xie, Fei

doi:10.1364/oe.21.013442

Cited by 80 publications

(37 citation statements)

References 11 publications

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“…The centre extraction methods are aimed at obtaining the centre positions of light stripes. They include the methods of extreme value [13,14], threshold [15][16][17], directional template [18][19][20], grey centroid [21,22], curve fitting [23,24] and Hessian matrix [25][26][27]. Below we discuss in brief these methods and point to their advantages and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

“…The centres of the light stripes are distinguished by the features of the two eigenvalues of the Hessian matrix, and the subpixel centre coordinate of the light stripe is computed by implementing Taylor expansion in the normal-vector direction. The method is known for its immunity to noise, high accuracy and robustness [25][26][27] and manifests advantages when processing the images of complex environment. It uses eigenvalue thresholds for adapting different light stripes and extracts subpixel-level centre positions of the centres of light stripes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive centre extraction method for structured light stripes

Hu¹,

Zhu²,

Hu³

et al. 2017

Ukr. J. Phys. Opt.

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Abstract. In the vision systems based on structured light, accurate extracting of centre positions of a light stripe represents one of the key points in solving the whole measurement task. Usually, high radiant illumination and objects with abundant surface textures decrease the detection precision. To address these problems, we suggest an adaptive centre-extraction method. First, it improves the image contrast using a novel adaptive threshold-based power transformation. Second, pixel-level centre points are obtained with our adaptive dual-threshold Canny's edge detection method. Finally, subpixel-level centre points are extracted using a Hessian matrix for a limited number of pixels. Our experiments prove robustness and practicability of the method, which can cope with complex surface textures of projected objects and high radiant illumination.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive centre extraction method for structured light stripes

Hu¹,

Zhu²,

Hu³

et al. 2017

Ukr. J. Phys. Opt.

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show abstract

“…Because the laser stripe center is a unique feature in the images, the extraction accuracy of the laser stripe center is a decisive factor for measurement accuracy. [11][12][13] However, due to the large size of aircraft components, the laser stripe covers a long scan range. In addition to variations due to multiple lighting effects (illumination, reflectivity of object, light source characteristics, etc.…”

Section: Introductionmentioning

confidence: 99%

Accuracy improvement in laser stripe extraction for large-scale triangulation scanning measurement system

Zhang

Liu

et al. 2015

Opt. Eng

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Abstract. Large-scale triangulation scanning measurement systems are widely used to measure the threedimensional profile of large-scale components and parts. The accuracy and speed of the laser stripe center extraction are essential for guaranteeing the accuracy and efficiency of the measuring system. However, in the process of large-scale measurement, multiple factors can cause deviation of the laser stripe center, including the spatial light intensity distribution, material reflectivity characteristics, and spatial transmission characteristics. A center extraction method is proposed for improving the accuracy of the laser stripe center extraction based on image evaluation of Gaussian fitting structural similarity and analysis of the multiple source factors. First, according to the features of the gray distribution of the laser stripe, evaluation of the Gaussian fitting structural similarity is estimated to provide a threshold value for center compensation. Then using the relationships between the gray distribution of the laser stripe and the multiple source factors, a compensation method of center extraction is presented. Finally, measurement experiments for a large-scale aviation composite component are carried out. The experimental results for this specific implementation verify the feasibility of the proposed center extraction method and the improved accuracy for large-scale triangulation scanning measurements. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…One category is based on pixel level, such as the extreme-value method [15,16], threshold method [17][18][19], and directional-template method [20][21][22]; the other category is executed at the subpixel level, including the gray-centroid method [23,24], curve fitting [25,26], and the Hessian-matrix method [27][28][29].…”

mentioning

confidence: 99%

“…Additionally, the actual grayscale distribution of pixels in a laser line is not strictly symmetrical, so the extreme point found via the curve-fitting process often deviates from the actual center of the laser line. The Hessian-matrix method [27][28][29] determines the centers of a laser line by analyzing the Hessian-matrix eigenvalues of the candidate feature points in the laser line. First, the centers of the laser line are distinguished by the features of the two eigenvalues of the Hessian matrix.…”

mentioning

confidence: 99%

Adaptable Center Detection of a Laser Line with a Normalization Approach using Hessian-matrix Eigenvalues

Xu¹,

Sun²,

Li³

et al. 2014

Journal of the Optical Society of Korea

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In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

show abstract

Statistical behavior analysis and precision optimization for the laser stripe center detector based on Steger's algorithm

Cited by 80 publications

References 11 publications

Adaptive centre extraction method for structured light stripes

Adaptive centre extraction method for structured light stripes

Accuracy improvement in laser stripe extraction for large-scale triangulation scanning measurement system

Adaptable Center Detection of a Laser Line with a Normalization Approach using Hessian-matrix Eigenvalues

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