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Local adaptive mismatching point elimination method based on radial basis function fitting

A local self-adaptive and mis-matched point technology, applied in the field of computer vision, can solve the problems of partial DLT method failure and inability to estimate the homography relationship

Active Publication Date: 2019-07-23
NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in areas where the number of feature points is insufficient, if the number of matching feature points in the neighborhood of the current feature point is less than 4, a reliable homography cannot be estimated, which will further lead to the failure of the local DLT method.

Method used

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  • Local adaptive mismatching point elimination method based on radial basis function fitting
  • Local adaptive mismatching point elimination method based on radial basis function fitting
  • Local adaptive mismatching point elimination method based on radial basis function fitting

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Embodiment Construction

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] The invention proposes a local adaptive error matching point elimination method based on radial basis function fitting. First, according to the given matching points in the two images, establish the global projection transformation relationship between the images, and calculate the projection deviation of each pair of projection points under the global projection; then, on the basis of the matching point projection deviation,...

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Abstract

The invention provides a local adaptive mismatching point elimination method based on radial basis function fitting, which comprises the following steps of establishing a global projection transformation relation between images according to given matching points in the two images, and calculating projection deviation of each pair of projection points under global projection; based on the projection deviation of the matching point, fitting by utilizing a radial basis function to obtain a deviation function of any position of the image and a weight coefficient thereof; and according to the probability distribution characteristics of the weight coefficients, gradually eliminating mistakenly matched points in a cyclic approximation manner. According to the method, based on the projection errorbetween matching points under global projection transformation, through kernel function smoothing based on a radial basis function, the projection deviation function of any position of the image canbe obtained through fitting, and local self-adaptive mismatching point elimination is achieved through the probability distribution characteristics of weight coefficients in the projection deviation function.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a method for eliminating mismatching points, in particular to a method and system for eliminating mismatching points based on radial basis function fitting. Background technique [0002] Image matching remains one of the hard problems in computer vision that has not yet been well-solved until now. In fact, no matter what kind of matching method is used, accurate and reliable matching results cannot be obtained due to the lack of necessary texture information in some local areas of the image. Even if the sparse feature point matching with relatively high reliability is used, the occurrence of false matching cannot be completely avoided. Therefore, after obtaining the preliminary matching results, it is very necessary to use certain image prior information to eliminate mismatching points. [0003] Random Sample Consensus (RANSAC) is currently the most commonly used method for removing...

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Application Information

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IPC IPC(8): G06T3/00
CPCG06T3/14
Inventor 邓宝松李靖印二威张周桂健钧闫野
Owner NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
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