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.
PyTorch implementation of the descriptor DEAL presented at NeurIPS 2021 "Extracting Deformation-Aware Local Features by Learning to Deform".
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
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
Functional interpolation to create a more meaningful interpolation than bilinear or bicubic methods.
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.
Python implementation of an end-to-end pipeline for swapping faces from images and videos using classical and deep learning approach
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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