计算机科学
人工智能
卷积神经网络
水准点(测量)
模式识别(心理学)
匹配(统计)
特征(语言学)
图像(数学)
特征提取
计算机视觉
图像配准
特征向量
数学
统计
大地测量学
哲学
语言学
地理
作者
Wang Xie,Hongxia Gao,Zhanhong Chen
标识
DOI:10.1007/978-3-030-03335-4_25
摘要
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.
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