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:
- Active Appearance Model (AAM)
- Holistic, Patch-based, Masked, Linear, Linear Masked
- Lucas-Kanade Optimisation
- Cascaded-Regression Optimisation
- Active Pictorial Structures (APS)
- Weighted Gauss-Newton Optimisation with fixed Jacobian and Hessian
- Active Template Model (ATM)
- Holistic, Patch-based, Masked, Linear, Linear Masked
- 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
- Constrained Local Model (CLM)
- 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.