A Digital Image Corner Detection Method Based on Radon Transform
A corner detection and digital image technology, applied in the field of data processing, can solve the problems of image noise sensitivity, large computational load, insufficient rapidity, etc., to improve detection speed, ensure positioning accuracy, and reduce computational time and space complexity Effect
Inactive Publication Date: 2019-03-01
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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[0004] The detection algorithm based on the image grayscale autocorrelation function uses the grayscale variation between the image window and its translation window to detect feature points. The advantage of this algorithm is that it does not need to calculate the image gradient and high-order differential. Its disadvantages are very sensitive to image noise
[0007] Among the detection methods for corner points, Harris detection operator, CSS detection operator, SIFT detection operator, etc. are commonly used. Although these operators have high precision, they all need to traverse all pixels in the image, and there is a large amount of calculation. Disadvantages such as large size and insufficient speed
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[0031] The invention provides a digital image corner detection method based on Radon transformation: the method can be implemented in a computer software environment, and the flow chart is as attached figure 1 As shown, the specific processing steps are as follows:
[0032] Step (1): Input an image and convert it into a grayscale image, use an edge extraction operator (such as sobel, canny, etc.) to perform edge extraction on the grayscale image, and obtain an edge extraction image;
[0033] Step (2): Carry out Radon transformation to the edge extraction image to obtain a Radon energy map, wherein the horizontal axis of the image is the transformation angle θ of the Radon transformation, and the vertical axis ρ is the distance from the origin to the straight line in the edge extraction image (the origin of the edge extraction image extract the center point of the image for the edge);
[0034] Step (3): On the Radon energy map, select the point whose brightness value is greate...
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The invention provides a digital image corner point detection method based on Radon transform, so as to increase corner point detection precision and reduce information processing workload. The digital image corner point detection method is characterized by comprising the steps of: a, performing edge extraction on an original image to obtain an edge extracted image; b, performing Radon transform on the edge extracted image to obtain a Radon energy diagram; c, selecting points with luminance values greater than a set threshold value from the Radon energy diagram, so as to extract coordinates (rho i, theta i) of a luminance extremum point; d, calculating coordinates of potential corner points; e, and judging authenticity of each potential corner point to obtain real corner point coordinates. The digital image corner point detection method based on Radon transform does not need to traverse all pixels in the image during corner point detection process, only needs to calculate elements between different classes after clustering, can ensure positioning precision of the corner points, and effectively reduces time and space complexity in calculation, thereby accelerating detection speed of the corner points.
Description
technical field [0001] The invention relates to a method for detecting corner points of a digital image, which is applicable to the fields of object recognition, image registration, three-dimensional reconstruction, virtual reality and the like, and belongs to the technical field of data processing. Background technique [0002] Image feature points usually refer to corner points, edge points with large curvature and block structure points in the image. Feature point detection is the automatic detection of image feature points by a computer through a detection algorithm. Feature point detection is the most basic problem in image analysis, understanding and computer vision, and it is the basis of many application fields (such as: image registration, 3D reconstruction, object recognition, motion tracking and video understanding, etc.). [0003] There are three main types of commonly used feature point detection methods: methods based on image grayscale, methods based on image ...
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IPC IPC(8): G06T7/13
CPCG06T2207/20164
Inventor 王旭光苏杰张楠
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)