A region-based descriptor network for uniformly sampled keypoints
This addresses a bottleneck in computer vision for researchers and practitioners by enabling more high-confidence matching points without custom extremum operations, though it is incremental.
The paper tackles the problem of matching keypoint pairs in images by proposing a region-based descriptor that works even in flat regions, eliminating the need for complex extremum point schemes. The method achieves performance comparable to state-of-the-art.
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.