Image Registration
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Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.
Source: Image registration | Wikipedia
( Image credit: Kornia )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hadiiiil/gnns-breastcompression
공식 코드Graph Neural Networks for modelling breast biomechanical compression
https://github.com/haizailache999/image-registration
공식 코드https://github.com/haoxu0507/ipmi25-sam-assisted-registration
공식 코드Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
https://github.com/hassaanmahmood/DeepIntense
공식 코드Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration
https://github.com/healthx-lab/cabld
공식 코드CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
https://github.com/healthx-lab/camld
공식 코드CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
https://github.com/heyblackC/DFMIR
공식 코드https://github.com/honkamj/locor
공식 코드https://github.com/honkamj/locor-experiments
공식 코드https://github.com/honkamj/sitreg
공식 코드https://github.com/hsokooti/RegNet
공식 코드https://github.com/hsokooti/regun
공식 코드Quantitative Error Prediction of Medical Image Registration using Regression Forests
https://github.com/huxiaoling/regre4regis
공식 코드Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
https://github.com/iamlab-ryerson/flowreg
공식 코드FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow
https://github.com/imfusiongmbh/disa-universal-multimodal-registration
공식 코드DISA: DIfferentiable Similarity Approximation for Universal Multimodal Registration
https://github.com/imr-framework/gdcnet
공식 코드https://github.com/isee-laboratory/panodecouple
공식 코드https://github.com/islab-sophia/2s-odis
공식 코드2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion Correction
https://github.com/iyerkrithika21/morph_ler
공식 코드MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
https://github.com/jaxwangkd04/IRON
공식 코드A Learning-based Optimization Algorithm:Image Registration Optimizer Network
https://github.com/jessychen1016/DPCN
공식 코드Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
https://github.com/jh-chung1/imgregister2dto3d
공식 코드https://github.com/jianqingzheng/def_diff_rec
공식 코드https://github.com/jianqingzheng/res_aligner_net
공식 코드https://github.com/jimas95/CMinMax
공식 코드cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope
https://github.com/jpdefrutos/ddmr
공식 코드https://github.com/jr-xing/lamod
공식 코드LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation
https://github.com/juliuswang-7/encoderreg
공식 코드Encoding Matching Criteria for Cross-domain Deformable Image Registration
https://github.com/juliuswang-7/pan
공식 코드https://github.com/junyuchen245/Fully_Unsupervised_CNN_Registration_Keras
공식 코드https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration
공식 코드TransMorph: Transformer for unsupervised medical image registration
https://github.com/junyuchen245/ViT-V-Net_for_3D_Image_Registration_Pytorch
공식 코드ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
https://github.com/junyuchen245/correlation_ratio
공식 코드Unsupervised Learning of Multi-modal Affine Registration for PET/CT
https://github.com/junyuchen245/pretraining_image_registration_dnns
공식 코드Pretraining Deformable Image Registration Networks with Random Images
https://github.com/jw4hv/HierarchicalBayesianAtlasBuild
공식 코드Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.