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Homography Estimation

Homography estimation is a technique used in computer vision and image processing to find the relationship between two images of the same scene, but captured from different viewpoints. It is used to align images, correct for perspective distortions, or perform image stitching. In order to estimate the homography, a set of corresponding points between the two images must be found, and a mathematical model must be fit to these points. There are various algorithms and techniques that can be used to perform homography estimation, including direct methods, RANSAC, and machine learning-based approaches.

Papers

Showing 71–80 of 134 papers

TitleStatusHype
AffineGlue: Joint Matching and Robust Estimation—0
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the EdgeCode0
Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature for Local Feature Matching-Based Homography Estimation—0
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
Analyzing the Domain Shift Immunity of Deep Homography EstimationCode0
Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial VideoCode0
Medical Image Analysis using Deep Relational Learning—0
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment—0
Nonlinear constructive observer design for direct homography estimation—0
ParaFormer: Parallel Attention Transformer for Efficient Feature Matching—0
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