Template Matching
57 papers with code • 0 benchmarks • 0 datasets
Template matching is a technique that is used to find a subimage or a patch (called the template) within a larger image. The basic idea behind template matching is to slide the template image over the larger image and compare the template to each portion of the larger image to determine the similarity between the template and the corresponding portion of the larger image.
Benchmarks
These leaderboards are used to track progress in Template Matching
Latest papers
scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching
Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context.
Autoregressive Visual Tracking
miv-xjtu/artrack
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CVPR 2023 2023
We present ARTrack, an autoregressive framework for visual object tracking.
Bridging Search Region Interaction With Template for RGB-T Tracking
To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts.
Score-based denoising for atomic structure identification
llnl/graphite
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We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.
AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System
We propose AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melody.
In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components
A deep learning segmentation of the pins is performed and the inspection pose is found by simulation.
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
nv-nguyen/template-pose
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CVPR 2022
It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects.
Affine Medical Image Registration with Coarse-to-Fine Vision Transformer
Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods.
Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow
siwensun/neural_diffeomorphic_flow--ndf
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CVPR 2022
Recently DIFs-based methods have been proposed to handle shape reconstruction and dense point correspondences simultaneously, capturing semantic relationships across shapes of the same class by learning a DIFs-modeled shape template.
