“…We used the crosswind least-squares algorithm to position the center of the cross-hair based on the Sobel operator and compared it with the proposed method. The leastsquares center-positioning algorithm for the crosshair based on the Sobel operator (SobelLSCP) is a two-step method that first uses the Sobel operator for edge detection and then carries out least-squares straight-line fitting according to the four edges of the crosshair to locate the center [3].…”
Section: Results and Analysismentioning
confidence: 99%
“…As a particular cooperative target, the crosshair mark has been used as a carrier for information transmission (for instance, to represent direction) when performing measurements on images, such as the deviation in the center of an optical lens system [1,2], the three-dimensional (3D) deformation of images of ships [3], and angle-measuring instruments in digital metrology (such as the theodolite or the laser plummet) [4,5] . A fundamental requirement for performing such image measurements is the appropriate positioning of sub-pixels representing the center of the crosshair.…”
Section: Introductionmentioning
confidence: 99%
“…While the centroid localization algorithm obtains a least-squares linear fit based on the center of the cross-section or the vertical section at different locations in its second step, the sub-pixel edge detection algorithm obtains a linear fit with the four edges [11,12] by taking the intersection of two straight lines as the center of the crosshair. Examples of such fitting algorithms include the polynomial [10] and the Gaussian fitting algorithms [3].…”
In this study, the authors propose a robust one-step algorithm to position the center of the crosshair for image measurement based on gray weighting (or gray-squared weighting) (GWRCP, GSWRCP) and axisymmetric straight-line fitting, which is a crucial part of the positioning algorithm. The proposed algorithm is also capable of making robust estimations. The results of simulations to test it showed that error in the GWRCP and GSWRCP was below 0.01 pixels following the addition of different levels of Gaussian noise, and was below 0.05 pixels following the addition of the gross error. The outcomes of examples of its application showed that the GWRCP/GSWRCP with an error smaller than 0.01 pixels exhibited superior resistance to noise and the gross error than the method that involves artificially removing the latter.
“…We used the crosswind least-squares algorithm to position the center of the cross-hair based on the Sobel operator and compared it with the proposed method. The leastsquares center-positioning algorithm for the crosshair based on the Sobel operator (SobelLSCP) is a two-step method that first uses the Sobel operator for edge detection and then carries out least-squares straight-line fitting according to the four edges of the crosshair to locate the center [3].…”
Section: Results and Analysismentioning
confidence: 99%
“…As a particular cooperative target, the crosshair mark has been used as a carrier for information transmission (for instance, to represent direction) when performing measurements on images, such as the deviation in the center of an optical lens system [1,2], the three-dimensional (3D) deformation of images of ships [3], and angle-measuring instruments in digital metrology (such as the theodolite or the laser plummet) [4,5] . A fundamental requirement for performing such image measurements is the appropriate positioning of sub-pixels representing the center of the crosshair.…”
Section: Introductionmentioning
confidence: 99%
“…While the centroid localization algorithm obtains a least-squares linear fit based on the center of the cross-section or the vertical section at different locations in its second step, the sub-pixel edge detection algorithm obtains a linear fit with the four edges [11,12] by taking the intersection of two straight lines as the center of the crosshair. Examples of such fitting algorithms include the polynomial [10] and the Gaussian fitting algorithms [3].…”
In this study, the authors propose a robust one-step algorithm to position the center of the crosshair for image measurement based on gray weighting (or gray-squared weighting) (GWRCP, GSWRCP) and axisymmetric straight-line fitting, which is a crucial part of the positioning algorithm. The proposed algorithm is also capable of making robust estimations. The results of simulations to test it showed that error in the GWRCP and GSWRCP was below 0.01 pixels following the addition of different levels of Gaussian noise, and was below 0.05 pixels following the addition of the gross error. The outcomes of examples of its application showed that the GWRCP/GSWRCP with an error smaller than 0.01 pixels exhibited superior resistance to noise and the gross error than the method that involves artificially removing the latter.
Accurate measurement of ship deformation angle is of great significance for building a unified space attitude reference and improving the combat performance of the entire ship. However, many problems existing in the actual sailing process restrict its measurement accuracy severely, such as the time delay between measurement equipment, the unknown characteristics of measurement noise and the unknown model parameters of the dynamic deformation. This paper proposes an adaptive variable parameter multiple model (AVPMM) algorithm that can simultaneously solve these problems. Firstly, a time delay compensation algorithm is derived to suppress the influence of time delay on measurement accuracy; then an adaptive Cubature Kalman filter (ACKF) is designed based on the idea of adaptive filtering to suppress the influence of unknown characteristics of measurement noise; and finally an AVPMM algorithm is designed by combining the above ideas to reduce the impact of unknown model parameters of dynamic deformation. The results of simulation and turntable experiment show that this algorithm has good measurement accuracy and the error is less than 0.15 ′ .
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