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Journal Article
Keypoint Detection Using Higher Order Laplacian of Gaussian
- Authors
- Yongju Cho, Dojin Kim, Saleh Saeed, Muhammad Umer Kakli, Soon-Heung Jung, Jeongil Seo, Unsang Park
- Issue Date
- 2020-01
- Citation
- IEEE Access, v.8, pp.10416-10425
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This paper presents a keypoint detection method based on the Laplacian of Gaussian (LoG). In contrast to the Difference of Gaussian (DoG)-based keypoint detection method used in Scale Invariant Feature Transform (SIFT), we focus on the LoG operator and its higher order derivatives. We provide mathematical analogies between higher order DoG (HDoG) and higher order LoG (HLoG) and experimental results to show the effectiveness of the proposed HLoG-based keypoint detection method. The performance of the HLoG is evaluated with four different tests: i) a repeatability test of the keypoints detected across images under various transformations, ii) image retrieval, iii) panorama stitching and iv) 3D reconstruction. The proposed HLoG method provides comparable performance to HDoG and the combination of HLoG and HDoG provides significant improvements in various keypoint-related computer vision problems.
- KSP Keywords
- 3d reconstruction, Computer Vision(CV), Detection Method, Difference of Gaussian(DoG), Higher-order, Image retrieval, Laplacian of Gaussian, LoG operator, keypoint detection, panorama stitching, scale invariant feature transform(SIFT)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
