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Template Matching
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/scientific-computing-nrcn/SimulAI
공식 코드Complete CVDL Methodology for Investigating Hydrodynamic Instabilities
https://github.com/siam1251/HandGestureRecognition
공식 코드Human Computer Interaction Using Marker Based Hand Gesture Recognition
https://github.com/siwensun/neural_diffeomorphic_flow--ndf
공식 코드Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow
https://github.com/skyoung/MemTrack
공식 코드https://github.com/smousavi05/CRED
공식 코드CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
https://github.com/sohambasu07/dr_2021
공식 코드https://github.com/sthalham/tram3d
공식 코드Self-supervised Vision Transformers for 3D Pose Estimation of Novel Objects
https://github.com/taohan10200/USADTM
공식 코드Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
https://github.com/ternaus/TernausNetV2
공식 코드TernausNetV2: Fully Convolutional Network for Instance Segmentation
https://github.com/thodan/bop_toolkit
공식 코드https://github.com/tygobl/meme-clustering
공식 코드Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity
https://github.com/wangbiao2/r1-track
공식 코드R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
https://github.com/wenbowen123/BundleTrack
공식 코드BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
https://github.com/xl-sr/THOR
공식 코드https://github.com/zhirui-gao/deep-template-matching
공식 코드Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement
https://github.com/zhongli-fan/nisr
공식 코드Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images
https://github.com/Goulustis/One_Class_MNIST
https://github.com/PINTO0309/Keras-OneClassAnomalyDetection
https://github.com/PaddlePaddle/PaddleOCR
Spatial Dual-Modality Graph Reasoning for Key Information Extraction
https://github.com/buiduchanh/Learning-Deep-Features-for-One-Class-Classification
https://github.com/halajun/diasss
Evaluation of a Canonical Image Representation for Sidescan Sonar
https://github.com/human-analysis/secure-face-matching
https://github.com/infantmotion/infantmotion.github.io
Unsupervised Human Pose Estimation through Transforming Shape Templates
https://github.com/johanofverstedt/sdt
https://github.com/jpmerc/DTOID
https://github.com/kamata1729/QATM_pytorch
https://github.com/llllxt/oneclass-deep-feature
https://github.com/luannd/MSU-LatentAFIS
https://github.com/lwhyc/oneshot_weaklyseg
https://github.com/min2209/dwt
https://github.com/minerva-ml/open-solution-mapping-challenge
TernausNetV2: Fully Convolutional Network for Instance Segmentation
https://github.com/neptune-ai/open-solution-mapping-challenge
TernausNetV2: Fully Convolutional Network for Instance Segmentation
https://github.com/prip-lab/MSU-LatentAFIS
https://github.com/timothyn617/watershed-transform
https://github.com/ujjwal1599/Vanilla-Bot
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