Image Feature Information Extraction for Interest Point Detection: A Review
원문 초록
Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description
Neural Outlier Rejection for Self-Supervised Keypoint Learning
USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds
SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.