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X corner point detection method applied to vision localization and calibration

A technology of corner detection and visual positioning, which is applied in the field of X corner detection, can solve the problems of large amount of algorithm calculation and not suitable for parallel batch processing, etc., and achieve the effect of enhancing robustness, improving operation speed, and improving detection speed

Active Publication Date: 2018-08-21
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The proposed method is mainly to judge the strength of the X corner point through various feature calculations. The algorithm has a large amount of calculation and is not suitable for parallel batch processing.

Method used

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  • X corner point detection method applied to vision localization and calibration
  • X corner point detection method applied to vision localization and calibration
  • X corner point detection method applied to vision localization and calibration

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] An X-corner detection method applied to visual positioning and calibration, such as figure 2 shown, including:

[0047] S1: Acquire images, using a paper window (such as figure 1 shown) to sample the image; set the side length of the paper-shaped window sampling to 2r pixels, and the paper-shaped window is a square, then the samples taken by the paper-shaped window contain 8r-4 pixels in total, and r is less than Half of the side length of the smallest X corner point in the image; count all the pixels of the paper window into a circular data queue, all the pixels of the paper window are the sample data P, record the i-th pixel as P i , P i The gray value of f i , i=1,2...(8r-4);

[0048] S2: According to the image characteristics of the X corner point, preliminarily judge whether the sample data P contains the X corner point, if the judgment condition is met, calculate the sub-pixel position of the X corner point, otherwise, go to step S5;

[0049] S3: According t...

Embodiment 2

[0053] According to an X corner point detection method applied to visual positioning and calibration described in Embodiment 1, the difference is that the step S2 includes:

[0054] S21: Grayscale the sample data in turn; the threshold can be selected adaptively.

[0055] S22: Binarize the gray value of the sample data twice, the binarization threshold is mean±Δ, mean is the mean value of the gray value of the sample data, Δ is the threshold adjustment value, and the value range of Δ is 20-160 pixels. The value of Δ is related to the brightness of the entire image, adding Δ as the threshold adjustment value can avoid wrong judgments caused by the influence of image noise and enhance the robustness of the algorithm. Calculate the number of steps N of the sample data processed in step S21 s , if N s =4, then execute step S23, otherwise, execute step S5;

[0056] S23: Binarize the gray value of the sample data by using the average value of the gray value of the sample data as...

Embodiment 3

[0059] According to an X corner point detection method applied to visual positioning and calibration described in Embodiment 1, the difference is that the step S3 includes:

[0060] S31: Determine the repeated detection flag of the X corner point, if the pixel value L of the X corner point obtained in step S23 is located in the inactive area, then it is determined that the X corner point has been detected, then jump out of this loop, and execute step S5; otherwise, Execute step S32;

[0061] S32: Obtain the grayscale value of the pixel value L of the X corner point neighborhood pixel, the neighborhood refers to the range with the pixel value L of the X corner point as the center and r pixels as the radius; the neighborhood grayscale value The mean of the neighborhood is used as the threshold to binarize the neighborhood, and the step number ΔV of the gray value is calculated C , if ΔV C >min_V, continue to execute step S4, otherwise, execute step S5; min_V=4.

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Abstract

The invention relates to an X corner point detection method applied to vision localization and calibration. The method comprises the steps of S1, collecting an image, and sampling the image by adopting annular and square windows; S2, according to image characteristics of X corner points, preliminarily judging whether sample data comprises the X corner points or not; S3, further judging whether thesample data comprises the X corner points or not, and eliminating the repeatedly judged X corner points; S4, by taking the X corner points as window centers, re-obtaining the sample data, judging whether the data meets an X corner point symmetry condition or not, and if yes, calculating out sub-pixel-level positions of the X corner points by using a curve fitting method, and setting an X corner point repeated detection mark; and S5, repeating the steps S2-S4, and detecting out all the X corner points. During image sampling, half of a window side length is subjected to interval sampling each time, so that the detection speed is increased and the X corner points are not omitted. Whether the sampling windows comprise the X corner points or not is judged based on the image characteristics ofthe X corner points, so that the robustness of an algorithm is enhanced.

Description

technical field [0001] The invention relates to an X corner point detection method applied to visual positioning and calibration, and belongs to the technical field of computer vision applications. Background technique [0002] Visual localization and calibration are important components of 3D computer vision. One of the basic tasks of computer vision is to calculate the geometric information of the object in the three-dimensional space from the image information obtained by the camera, and then reconstruct and recognize the object, and the three-dimensional geometric position of a point on the surface of the space object and its corresponding point in the image The interrelationship of is determined by the geometric model of camera imaging, and these geometric model parameters are camera parameters. Under most conditions, these parameters must be obtained through experiments and calculations. This process is called visual calibration. The calibration process is to determi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/80G06T7/60
CPCG06T7/60G06T2207/10016G06T2207/30208G06T7/80
Inventor 赵子健王芳
Owner SHANDONG UNIV
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