Research on feature point generation and matching method optimization in image matching algorithm

Image matching is a basic problem in image processing and pattern recognition. It is used to calculate the visual similarity between images taken in the same scene with different sensors, different perspectives or at different times. In addition to image adjustment, it is an indispensable step in image analysis and digital photogrammetry. It is also important for applications such as automatic navigation, image processing, medical image analysis, and motion estimation. The current image adjustment technology can be divided into three categories: domain-based image conversion technology, gray-scale-based technology, and performance-based technology. Among them, the feature-based matching algorithm directly matches the features of the image, so it greatly improves the calculation efficiency and is easy to adapt to complex image transformations, such as geometric distortion, different resolutions, and image transformations at different angles. Image matching refers to the process of using effective matching algorithms to find the same or similar cue points for two or more image data. In applications such as medical image processing and analysis, remote sensing monitoring, weapon movement and image processing, image matching technology is an important step. Images have strong structural features, such as corners, edges, statistics, and textures. These functions play an important role in image matching and scanning technology. The key to many image matching problems depends on selection, detection and expression. For different image matching problems, different functions are selected, and the matching results may be very different.