Method for matching images of different sources based on fit contour

Abstract

The invention discloses a method for matching images of different sources based on a fit contour. The method is characterized by comprising the following steps of: first, in order to overcome noises, such as transformation, mutation, burr and the like, of metric space, performing curvature scale space-based contour denoising and fitting processing on extracted contours to obtain smooth contours on the same scale; then, performing centroid distance-based contour description on closed contours, and performing local curvature-based contour description on contours formed by open curves; next, establishing RST (rotating, scaling and translating) feature invariants on the contour description; and finally, performing corresponding reconstruction on the feature invariants to obtain a final matching point pair. By the method, the matching of the images of the different sources based on the contour is realized; and good robustness on the matching of the images of the different sources with rotating, translating and scaling changes is achieved.

Description

technical field [0001] The invention relates to a heterogeneous image matching method in the vicinity of remote sensing, pattern recognition, medical diagnosis, computer vision, etc., and belongs to the technical field of image matching. Background technique [0002] Image matching is a key technology in visual navigation, pattern recognition, guidance, terrain measurement and medical diagnosis based on heterogeneous images of remote sensing satellites, aircraft, and missiles. Image matching is used to match two or more visible light, infrared and SAR images or heterogeneous images of the same scene obtained at different imaging times, different imaging devices and different imaging conditions. Image matching is an indispensable key step in practical applications such as image fusion, retrieval, correction, mosaic and target recognition, and its matching accuracy and robustness directly affect the results of subsequent processing. Therefore, it has always been the goal that...

AI Technical Summary

Problems solved by technology

However, this method cannot better match the potential partial contour with the overall contour

[0005] However, due to the difference in imaging time (such as day and night), the difference in imaging conditions (such as weather, illumination and other external factors), the large difference in imaging principles (such as optical and SAR images), and the large difference in wavelength bands (such as visible light and infrared images), It is difficult to obtain the common characteristics of the image in terms of grayscale, brightness, color and other characteristics

Therefore, there are still many difficulties in the research of heterogeneous image matching with different resolutions, and the matching of heterogeneous images is still in its infancy.

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