Image matching method

Abstract

The invention discloses an image matching method and belongs to the technical field of image matching. The method comprises: Utilize MSER method to detect a homogeneous region in an image for matching; utilize an ellipse region function to perform fitting for each detected homogeneous region; utilize an ellipse gradient bar chart and multi-dimensional image decomposition to extract a characteristic vector of the region under test in the homogeneous area after the fitting with the ellipse region function; utilize a binary tree in a layered k mean clustering algorithm structure homogeneous region, in order to perform image matching. The method also comprises: Utilize a progressive method combined with a binary tree to construct a categorizer for the interested area, in order to perform image matching. The invention combines the image dimensional space with a statistics learning method, so as to considerably lower complexity of the characteristic vector of the extracted images and local region matching complexity, and meanwhile, strengthen stability of the image matching algorithm.

Description

technical field [0001] The invention relates to the technical field of image matching, in particular to an image matching method. Background technique [0002] Image matching is a very important research topic in computer vision, widely used in image registration, object detection, object recognition and image retrieval and other fields. In the process of image acquisition, factors such as scale, viewing angle and illumination changes, and partial occlusion usually cause significant differences between different observation images of the same scene, which is where the difficulty of image matching lies. In order to solve the above difficult problems, the image matching method based on the local area is an effective method. At present, most of the local region detection methods are based on whether the local changes in the image are significant or not for stable region detection. According to transformation invariance, current local region detection methods can be divided in...

AI Technical Summary

Problems solved by technology

[0004] However, when using MSER stable regions for image matching, there are the following three problems that need to be solved: 1) There is a certain overlap between MSER regions, resulting in weak discrimination between different regions; 2) The number of MSER regions is relatively small. There are many and large areas, and the time cost of extracting features is very high; 3) The time complexity of region matching is very high (proportional to the product of the number of stable regions in the two images)

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