Efficient keypoint detection and description via polynomial regression of scale space
Ryo Okutani, Kenjiro Sugimoto, Sei-ichiro Kamata
Abstract
Keypoint detection and description using approximate continuous scale space are more efficient techniques than typical discretized scale space for achieving more robust feature matching. However, this state-of-the-art method requires high computational complexity to approximately reconstruct, or decompress, the value at an arbitrary point in scale space. Specifically, it has O(M2) computational complexity where M is an approximation order. This paper presents an efficient scale space approach that provides decompression operation with O(M) complexity without a loss of accuracy. As a result of the fact that the proposed method has much fewer variables to be solved, the least-square solution can be obtained through normal equation. This is easier to solve than the existing method which employs Karhunen-Loeve expansion and generalized eigenvalue problem. Experiments revealed that the proposed method performs as expected from the theoretical analysis.
BibTeX
@inproceedings{icassp2016_efficientkeypoin,
title = {Efficient keypoint detection and description via polynomial regression of scale space},
author = {Ryo Okutani and Kenjiro Sugimoto and Sei-ichiro Kamata},
booktitle = {ICASSP 2016},
year = {2016}
}