Image corner point matching method based on self-adaptive threshold and RANSAC

An adaptive threshold and corner matching technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problem of difficulty in estimating the threshold size.

Inactive Publication Date: 2018-07-27
CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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Problems solved by technology

This algorithm improves the Harris corner response, but there is still the problem that

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  • Image corner point matching method based on self-adaptive threshold and RANSAC
  • Image corner point matching method based on self-adaptive threshold and RANSAC
  • Image corner point matching method based on self-adaptive threshold and RANSAC

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Example Embodiment

[0068] Embodiment 1: The method of the present invention is described in detail below:

[0069] Harris corner detection principle

[0070] Harris corner detection algorithm is developed on the basis of Moravec algorithm, which is proposed by Harris C and Stephens MJ. Harris improved the Moravec corner detection algorithm by using differential operations and autocorrelation matrices. For an image I(x,y), the small window centered on a certain pixel (x,y) moves u in the x direction, and moves v in the y direction, and the gray intensity change given by Harris is shown in the formula (1 ) as shown:

[0071] E(x,y)=∑w(x,y)[f(x+u,y+v)-f(x,y)] 2 (1)

[0072] Among them, f(x,y) represents the gray value at point (x, y), f represents the gray level function Formula (1) is the definition of Harris algorithm, Harris represents the gray intensity change when the window moves, f is a representation symbol, which is convenient for the embodiment of program code. w(x,y) is a Gaussian...

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Abstract

The invention discloses an image corner point matching method based on a self-adaptive threshold and RANSAC. The image corner matching method comprises the following steps: firstly, pre-screening corner points by adopting a way of suppressing a non-maximum value by means of self-adaptation; secondly, performing secondary screening on the corner points by adopting an Forstner operator; then, performing coarse matching on detected Harris corner points by adopting a normalized cross-correlation matching algorithm; and finally, accurately matching images by using a random sampling unification algorithm. An experimental result proves that by adopting the improved method, the time required for detecting the corner points and matching the images is shortened, and the matching accuracy of the images can be effectively improved. The image corner point matching method based on the self-adaptive threshold and the RANSAC has the advantages of high processing efficiency and high matching accuracy.

Description

technical field [0001] The invention relates to an image corner point matching method based on adaptive threshold and RANSAC. Background technique [0002] Image registration is the process of matching and superimposing two or more images acquired at different times, with different sensors (imaging devices) or under different conditions (weather, illuminance, camera position and angle, etc.). It is widely used in remote sensing data analysis, computer vision, image processing and other fields. Image registration can be divided into two categories: patch matching based methods and feature matching based methods. Li et al. proposed a matching method based on block matching, which uses the entire image information to obtain high matching accuracy, but there are problems such as large amount of calculation and time-consuming based on block matching. Zhou proposed a registration method based on local features. Compared with the registration method based on global features propo...

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Application Information

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IPC IPC(8): G06T7/32
CPCG06T2207/20004G06T7/32
Inventor 秦姣华李浩向旭宇钟少宏马文涛
Owner CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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