Feature point matching method for close-range shot stereoscopic image

Abstract

The invention provides a feature point matching method for a close-range shot stereoscopic image. The feature point matching method for the close-range shot stereoscopic image comprises the steps that feature point positive matching is conducted on homonymous images with subregions as constrain conditions and with triangles as constraint conditions in sequence so that a positive homonymous image point cluster can be obtained, feature point reversal matching is conducted on the homonymous images with the subregions as the constrain conditions and with the triangles as the constraint conditions in sequence so that a reversal homonymous image point cluster can be obtained, matched homonymous image point results in the positive homonymous image point cluster and in the reversal homonymous image point cluster are reserved, and a final matched homonymous image point result is obtained. According to the feature point matching method for the close-range shot stereoscopic image, feature point detection is conducted with the subregions as the constraint conditions and with the triangles as the constraint conditions in sequence, the time efficiency of feature point detection is greatly improved, the number of correct matched points is improved, and the feature point matching method has high application value on the aspect of close-range shot stereoscopic image matching.

Description

technical field [0001] The invention belongs to the field of digital close-range photography and relates to a feature point matching method. Background technique [0002] With the development of modern surveying and mapping technology, digital photogrammetry can provide data and support for digital earth, three-dimensional modeling of cities, etc. For the original data obtained by digital photogrammetry, the features of digital images are more important, including point features, line features and surface features. The method of extracting these features is the basis of image analysis and image matching, and various operators can be used. For example, Moravec operator, Forstner operator and Harris operator can all be used to extract feature points. For obvious objects in digital images, it is not only necessary to identify them, but also to determine their positions. The measurement of stereo pairs in photogrammetry is the basis for extracting three-dimensional information...

AI Technical Summary

Problems solved by technology

However, when acquiring image data, due to the influence of factors such as precipitation reflection, soil movement, high-speed camera focal length setting and calibration accuracy, shooting angle, and scene layout, the image data is prone to blurring, inconspicuous features, and blurred textures. At this time, if the conventional image matching method is used, there will be fewer correct matching points and more wrong matching points.

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