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| Improved SIFT Feature Matching Algorithm with Geometric Feature Constraints |
| ZHANG Xi-min1,2,ZHAN Hai-sheng2,YU Qi-ying2 |
1. School of Information Engineering, Shaanxi Institute of International Trade & Commerce, Xi’an,
Shaanxi 712046, China
2. School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi 710071, China |
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Abstract The accuracy of image registration directly affects the accuracy of machine vision dimension detection. Aiming at the fact that scale-invariant feature transform(SIFT) feature transform algorithm was less involved the geometric relationship between feature points, and it was easy to generate mismatch points for the capture image with smooth varied grayscale, such as small widget, an improved SIFT feature matching algorithm with geometric feature constraints was proposed. Firstly, the target contour profile was extracted by the image tracking algorithm based on boundary topology analysis. Secondly, the SIFT algorithm was constrained by geometric contour profiles of images, and the Random sample consensus(RANSAC) algorithm was taken to remove outlier point pairs. Finally, image accurate registration transformation was performed by calculated transformation matrix. A machine vision precision detection and measurement system were developed based on high-resolution industrial camera and high-performance computer, and experiments were carried out on the USB interface plug-in parts of mobile phone as samples. The experimental results showed that the accuracy of image registration of this algorithm can reach 95.31%. Compared with SIFT algorithm and SIFT+RANSAC algorithm, the registration accuracy was greatly improved. The algorithm has been applied to develop an automation dimension detection and measurement system.
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Received: 04 August 2022
Published: 22 August 2023
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2023, Vol. 44 
