Image Registration Based on Patch Matching Using a Novel Convolutional Descriptor
In this paper we introduce a novel feature descriptor based on deep learning that trains a model to match the patches of images on scenes captured under different viewpoints and lighting conditions. The patch matching of images capturing the same scene in varied circumstances and diverse manners is challenging. Our approach is influenced by recent success of CNNs in classification tasks. We develop a model which maps the raw image patch to a low dimensional feature vector. As our experiments show, the proposed approach is much better than state-of-the-art descriptors and can be considered as a direct replacement of SURF. The results confirm that these techniques further improve the performance of the proposed descriptor. Then we propose an improved Random Sample Consensus algorithm for removing false matching points. Finally, we show that our neural network based image descriptor for image patch matching outperforms state-of-the-art methods on a number of benchmark datasets and can be used for image registration with high quality.
Feature descriptor Deep learning Patch matching
Wang Xie Hongxia Gao Zhanhong Chen
School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,Peoples Republic of China
国际会议
广州
英文
285-296
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）