DKM: Dense Kernelized Feature Matching for Geometry Estimation [PDF]
Computer Vision and Pattern Recognition, 2022 Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all ...
Johan Edstedt +3 more
semanticscholar +1 more source
MatchFormer: Interleaving Attention in Transformers for Feature Matching [PDF]
Asian Conference on Computer Vision, 2022 Local feature matching is a computationally intensive task at the subpixel level. While detector-based methods coupled with feature descriptors struggle in low-texture scenes, CNN-based methods with a sequential extract-to-match pipeline, fail to make ...
Qing Wang +4 more
semanticscholar +1 more source
DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching [PDF]
Expert systems with applications, 2023 Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes.
Tao Xie +4 more
semanticscholar +1 more source
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors [PDF]
ACM Multimedia, 2022 A key challenge of real-world image super-resolution (SR) is to recover the missing details in low-resolution (LR) images with complex unknown degradations (\eg, downsampling, noise and compression).
Chaofeng Chen +6 more
semanticscholar +1 more source
SuperGlue: Learning Feature Matching With Graph Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2019 This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Paul-Edouard Sarlin +3 more
semanticscholar +1 more source
PATS: Patch Area Transportation with Subdivision for Local Feature Matching [PDF]
Computer Vision and Pattern Recognition, 2023 Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences.
Junjie Ni +6 more
semanticscholar +1 more source
A novel method for SIFT features matching based on feature dimension matching degree [PDF]
MATEC Web of Conferences, 2019 We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding ...
Yang Yao +3 more
doaj +1 more source
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching [PDF]
Computer Vision and Pattern Recognition, 2023 Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency.
Jiahuan Yu +4 more
semanticscholar +1 more source
Feature Matching Data Synthesis for Non-IID Federated Learning [PDF]
IEEE Transactions on Mobile Computing, 2023 Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server.
Zijian Li +5 more
semanticscholar +1 more source
A Method to Enhance Homogeneous Distribution of Matched Features for Image Matching
International Journal of Environment and Geoinformatics, 2020 Features are distinctive landmarks of an image. There are various feature detection and description algorithms. Many computer vision algorithms require matching of features from two images.
Baran Gulmez +5 more
doaj +1 more source