PyTorch implementation of Spatial Transformer Network (STN) with Thin Plate Spline (TPS)
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Updated
Jul 15, 2021 - Python
PyTorch implementation of Spatial Transformer Network (STN) with Thin Plate Spline (TPS)
DALF is a joint image keypoint detector and descriptor for handling non-rigid deformations. It also works great under large rotations.
Python (and Cython) implementation of the publication of Bookstein, F. L. (1989). Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11 (6), pp. 567-585
PyTorch implementation of the descriptor DEAL presented at NeurIPS 2021 "Extracting Deformation-Aware Local Features by Learning to Deform".
Geometric Morphometrics in Python
Interactive thin-plate spline interpolation
Source code for R package chebpol on CRAN
C# / .NET wrapper for thin plate spline interpolation based on Peter Stroia-Williams C++ implementation
Python code to swap faces from images and videos using classical and deep learning approach.
CMSC733 - Computer Processing Pictorial Information: End-to-end pipeline to swap faces in videos and images using warping methods Delaunay Triangulation and Thin Plate Spline
Python implementation of an end-to-end pipeline for swapping faces from images and videos using classical and deep learning approach
Implementation of face swapping using classical and deep learning technique.
A python implementation of an end-to-end pipeline for swapping faces from images and videos using classical and deep learning approach.
Functional interpolation to create a more meaningful interpolation than bilinear or bicubic methods.
Computationally efficient Bayesian variable selection method to identify linear and nonlinear effects in regression models where a predictor (or feature) can have a zero effect, a non-zero linear effect or non-zero non-linear effect on the outcome variables.
Implementing a Python-based project using computer vision to analyse a wooden beer bottle cap display (shaped like Germany), aiming to correlate bottle caps with their geographic references by minimizing spatial distances between map holes and physical locations.
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