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/AnasIbrahim/image_agnostic_segmentation
공식 코드DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping
https://github.com/FrankZijlstra/SmartTracking
공식 코드https://github.com/HYOJINPARK/TTVOS
공식 코드https://github.com/LPXTT/GradNet-Pytorch
공식 코드https://github.com/LPXTT/GradNet-Tensorflow
공식 코드https://github.com/LWHYC/RPR-Loc
공식 코드https://github.com/MIDA-group/sdt
공식 코드https://github.com/PramuPerera/DeepOneClass
공식 코드https://github.com/PrincetonUniversity/APPLEpicker
공식 코드APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
https://github.com/RalphMao/PatchNet
공식 코드PatchNet -- Short-range Template Matching for Efficient Video Processing
https://github.com/Stochastik-TU-Ilmenau/image-template-resolution
공식 코드Quantifying the Resolution of a Template after Image Registration
https://github.com/ZJU-Robotics-Lab/DPCN
공식 코드Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
https://github.com/aktgpt/vq-nnf
공식 코드Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields
https://github.com/anupsingh15/BEST-STD
공식 코드BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection
https://github.com/billyblu2000/accomontage2
공식 코드AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System
https://github.com/cmsflash/global-context-module
공식 코드Fast Video Object Segmentation using the Global Context Module
https://github.com/cplusx/QATM
공식 코드https://github.com/cwmok/C2FViT
공식 코드Affine Medical Image Registration with Coarse-to-Fine Vision Transformer
https://github.com/droneslab/mars
공식 코드MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain
https://github.com/fb82/miho
공식 코드Image Matching Filtering and Refinement by Planes and Beyond
https://github.com/fhagelskjaer/pin-segmentation
공식 코드In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components
https://github.com/iminfine/Deep-DIM
공식 코드Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN
https://github.com/insomnia94/FTMU
공식 코드Fast Template Matching and Update for Video Object Tracking and Segmentation
https://github.com/jessychen1016/DPCN
공식 코드Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
https://github.com/joeybgofficial/through-the-wall-radar-human-activity-recognition-without-using-neural-networks
공식 코드Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks
https://github.com/joshuadickey/seis-sim
공식 코드Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity
https://github.com/llnl/graphite
공식 코드https://github.com/lschmidtke/shape_templates
공식 코드Unsupervised Human Pose Estimation through Transforming Shape Templates
https://github.com/marslicy/cartilage-graph-based-us-ct-registration
공식 코드Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph
https://github.com/miv-xjtu/artrack
공식 코드ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
https://github.com/nv-nguyen/template-pose
공식 코드https://github.com/open-mmlab/mmocr
공식 코드Spatial Dual-Modality Graph Reasoning for Key Information Extraction
https://github.com/quangle2110/GAN_Mask-RCNN
공식 코드Instance Semantic Segmentation Benefits from Generative Adversarial Networks
https://github.com/quickhdsdc/Point-Tracking-for-Displacement-Measurement-in-Railway-Applications
공식 코드https://github.com/qvpr/vprsnn
공식 코드Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.