A region-based descriptor network for uniformly sampled keypoints

Autor:	Lv, Kai, Lu, Zongqing, Liao, Qingmin
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing 68T06 I.4
Druh dokumentu:	Working Paper
Popis:	Matching keypoint pairs of different images is a basic task of computer vision. Most methods require customized extremum point schemes to obtain the coordinates of feature points with high confidence, which often need complex algorithmic design or a network with higher training difficulty and also ignore the possibility that flat regions can be used as candidate regions of matching points. In this paper, we design a region-based descriptor by combining the context features of a deep network. The new descriptor can give a robust representation of a point even in flat regions. By the new descriptor, we can obtain more high confidence matching points without extremum operation. The experimental results show that our proposed method achieves a performance comparable to state-of-the-art. Comment: 5 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.01780 Zobrazit plný text záznamu View this record from Arxiv