基于深度学习的偏振图像局部特征提取算法研究(特邀)

Research on Local Feature Extraction Algorithm for Polarized Images Based on Deep Learning(Invited)

针对传统探测方式对目标探测不到、看不清、图像轮廓和细节模糊等问题,采用红外与偏振探测相结合方式,通过对红外图像信息和偏振图像信息解算,解决在各种环境下探测不到、看不清的问题。针对目标局部特征提取过程数据量大、提取速度慢等问题,提出了一种改进的深度学习偏振图像局部特征提取SIFT算法。实验结果显示,该算法结合偏振成像和深度学习的优势,实现在简单或复杂背景下目标的特征快速提取,该算法对偏振图像局部特征提取速度快、提取精度高。该算法为目标的分类、识别与跟踪技术奠定理论基础。

Aiming at the problems that the traditional detection method can't detect and see the target clearly,and the image contour and detail are blurred.The combination of infrared and polarization detection is adopted to solve the problem that the infrared image information and polarization image information cannot be detected and seen in various environments.Aiming at the problems of large amount of data and slow extraction speed in the process of target local feature extraction,an improved deep learning local feature extraction(SIFT)algorithm for polarization images is proposed.Experimental results show that the improved algorithm combines the advantages of polarization imaging and deep learning to achieve rapid feature extraction of targets in simple or complex backgrounds.This algorithm improves the speed and accuracy of local feature extraction of polarized images.The improved algorithm lays a theoretical foundation for target classification,recognition and tracking technology.