WelcomeΒΆ
Welcome to the MenpoFit documentation!
MenpoFit is a Python package for building, fitting and manipulating deformable models. It includes state-of-the-art deformable modelling techniques implemented on top of the Menpo project. Currently, the techniques that have been implemented include:
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Lucas-Kanade Optimisation
Cascaded-Regression Optimisation
Active Pictorial Structures (APS)
Weighted Gauss-Newton Optimisation with fixed Jacobian and Hessian
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Lucas-Kanade Optimisation
Lucas-Kanade Image Alignment (LK)
Forward Additive, Forward Compositional, Inverse Compositional
Residuals: SSD, Fourier SSD, ECC, Gradient Correlation, Gradient Images
Unified Active Appearance Model and Constrained Local Model (Unified AAM-CLM)
Alternating/Project Out with Regularised Landmark Mean Shift
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Active Shape Model
Regularised Landmark Mean Shift
Ensemble of Regression Trees (ERT) [provided by DLib]
Supervised Descent Method (SDM)
Non Parametric
Parametric Shape
Parametric Appearance
Fully Parametric
Please see the to References for an indicative list of papers that are relevant to the methods implemented in MenpoFit.