A region-based descriptor network for uniformly sampled keypoints
- URL: http://arxiv.org/abs/2103.01780v1
- Date: Tue, 26 Jan 2021 07:31:22 GMT
- Title: A region-based descriptor network for uniformly sampled keypoints
- Authors: Kai Lv, Zongqing Lu, Qingmin Liao
- Abstract summary: 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.
In this paper, we design a region-based descriptor by combining the context features of a deep network.
- Score: 35.394659345406865
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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