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| An Improved Image Registration Algorithm Based on SURF-ORB |
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Abstract Aiming at the problems of existing algorithms combining ORB with scale space, an improved algorithm is proposed. Establish SURF image pyramid, detect ORB feature points on it and use 256 bit binary characters as feature vectors to describe feature points. The nearest neighbor method matches feature points. Filter matching points by utilizing the similar properties of neighborhood average gray difference, Euclidean distance, matching point line and x-axis angle among the correct matching points. The K-means algorithm is improved, using points with a density greater than the threshold as the center point of the class, clustering, deleting classes with a sum of squared errors greater than the threshold and reclassifying the remaining feature points into the reserved classes. The RANSAC algorithm is improved, merging all transformation matrixes’ interior points into a set and classifying the matching points in the set that meet the error distance threshold with the candidate optimal transformation model into its inner points. Use the least squares method to recalculate the transformation matrix with all its interior points to obtain a more accurate solution. The experimental results show that the number of feature points extracted by this algorithm is about 26% less than that of SURF and ORB algorithm, the matching accuracy is improved by about 16%,and the operation time is reduced by about 26%.The algorithm proposed in this paper has better overall performance and stronger real-time performance.
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Received: 28 May 2024
Published: 19 March 2025
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2025, Vol. 46 
