Micro-target localization algorithm based on improved local contour extraction and feature point matching

Images at the micrometer level usually have high resolution and contain a large amount of detailed information, and traditional vision algorithms are designed for macroscopic images, making it difficult to achieve accurate target localization at the microscopic scale. In this paper, we propose a micro-target localization algorithm based on improved local contour extraction and feature point matching to address the problems of low accuracy and time-consuming operation point localization under microscopic vision due to uneven illumination, angular shift of micro-targets, and occlusion. In the horizontal perspective, a light source correction algorithm based on the morphological algorithm and an edge enhancement algorithm based on Fourier transform is proposed to improve the accuracy of threshold segmentation and edge extraction, and a contour feature extraction algorithm based on Normalized Cross-Correlation template matching and improved Otsu's thresholding method is utilized to achieve high-precision localization of multi-targets in micro-scale. In the vertical perspective, a Binary Robust Invariant Scalable Keypoints matching algorithm based on spatial feature screening is proposed to solve the problems of feature point mismatch and inaccurate localization of traditional algorithms in case of angular offset and occlusion of micro-targets. Finally, experiments were conducted on the microscopic vision operating system and experimentally compared with cutting-edge methods to verify the feasibility and superiority of the present method. The experimental results show that the proposed algorithm in this paper has an average error of 1.023 pixels and an average elapsed time of 109.08 ms, exhibits higher stability in the presence of light source interference, angular offset, and occlusion of micro-targets, and significantly improves both localization accuracy and efficiency.