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Detection method for black and white checkerboard image corners based on least square optimization

A black-and-white chessboard and least-squares technology, applied in image analysis, image data processing, instruments, etc., can solve the problem of missed detection of corners, ignoring SUSAN area, etc., to improve precision and accuracy, simple algorithm, real-time performance strong effect

Active Publication Date: 2014-08-20
NORTHEASTERN UNIV
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Problems solved by technology

However, the SUSAN method also has shortcomings: because it only considers the size of the SUSAN area when detecting corner points, and does not consider the shape of the SUSAN area, some special types of corner points may be missed.

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  • Detection method for black and white checkerboard image corners based on least square optimization
  • Detection method for black and white checkerboard image corners based on least square optimization
  • Detection method for black and white checkerboard image corners based on least square optimization

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Embodiment Construction

[0021] An embodiment of the present invention will be further described below in conjunction with the accompanying drawings.

[0022] The software environment of this embodiment is the WINDOWS7 system, the simulation environment is MATLAB R2014a, and the processor is the second generation Core i5-2410.

[0023] The flow chart of the detection method of the black and white checkerboard image corner point based on the least squares optimization of the present embodiment, as figure 1 shown, including the following steps:

[0024] Step 1. Convert the color checkerboard image into a grayscale checkerboard image.

[0025] Use the MATLAB R2014a system grayscale conversion function to convert the color checkerboard image into a grayscale checkerboard image, grayscale checkerboard image.

[0026] Step 2. Adjust the size of the binarization threshold so that each black grid in the gray-scale checkerboard image is separated from the surrounding four black grids, thereby determining the...

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Abstract

The invention provides a detection method for black and white checkerboard image corners based on least square optimization. According to the method, a colorful checkerboard image is converted into a grey level checkerboard image, binarization is performed on the checkerboard image to obtain a binarized image, the corner region is determined according to the binarized image and the grey level checkerboard image, a method of least square fit for circles is used for fitting the circles, the centers of the circles are the checkerboard image corners, and therefore a corner coordinate set is generated. According to the method, parameters for calibrating a camera lens are not needed, and the method is suitable for detecting any distorted black and white checkerboard corner and widely applied to the fields of camera calibration, panorama parking, visual detection and monocular distance measuring.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a method for detecting corner points of a distorted black and white checkerboard image based on least square optimization. Background technique [0002] The black and white checkerboard template has been widely used in the process of camera calibration to obtain the internal and external parameters of the lens. The accuracy of the parameters obtained in the early camera calibration determines the accuracy of various image applications in the later stage, and the corner position extraction of the black and white checkerboard calibration board The accuracy largely determines the accuracy of camera calibration. [0003] There are various methods of corner detection, but they can be roughly divided into four categories: corner detection based on edge features, corner detection based on grayscale images, corner detection based on binary images, and mathematical morphology. The commonly ...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 钟惟林张云洲朱德龙廖峭
Owner NORTHEASTERN UNIV
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