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framework [2017/06/27 12:34]
admin
framework [2020/10/09 16:29] (current)
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   * a module to estimates anthropometric dimensions based on a set of predictors (Anthropometric Prediction Module) and three public anthropometric databases from children to elderly. A functionality to predict anthropometric dimensions directly using the GEBOD regression is also included.   * a module to estimates anthropometric dimensions based on a set of predictors (Anthropometric Prediction Module) and three public anthropometric databases from children to elderly. A functionality to predict anthropometric dimensions directly using the GEBOD regression is also included.
   * the Scaling Constraints Module to interactively build correspondences between anthropometric dimensions and a HBM to prepare scaling. The module can also call all required modules to define the target and perform the transformation   * the Scaling Constraints Module to interactively build correspondences between anthropometric dimensions and a HBM to prepare scaling. The module can also call all required modules to define the target and perform the transformation
-  * a geometrical interpolation module to support model morphing (Kriging Module). The module integrates many numerical features useful within the context of HBM scaling (allows arbitrary number of control points, automatic control point decimation, weighting of the bone and skin, use of surface distance...)+  * a geometrical interpolation module to support model morphing (Kriging Module). The module integrates many numerical features useful within the context of HBM scaling (allows arbitrary number of control points, automatic control point decimation...)
   * a module (Scaling the PIPER child model by age) dedicated to the PIPER Child scalable model, which allows to generate models matching an age or a stature (based on the GEBOD regressions). The functionality to scale the material parameters with age is also included as an experimental feature.   * a module (Scaling the PIPER child model by age) dedicated to the PIPER Child scalable model, which allows to generate models matching an age or a stature (based on the GEBOD regressions). The functionality to scale the material parameters with age is also included as an experimental feature.
   * a Contour Deformation Module to transform the HBM using contour based approaches   * a Contour Deformation Module to transform the HBM using contour based approaches
 +
 +{{ :scalconstraint_example.png?nolink&300 |Scaling Constraint module with GHBMC}} {{ :child_scaling.png?nolink&400 |Child scaling module}}
  
 ===== Positioning in PIPER: overview ===== ===== Positioning in PIPER: overview =====
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 The PIPER framework aims to propose some alternative methodologies that can be used along current approaches. Positioning in PIPER typically starts with the Physics-based Interactive Pre-Positioning Module (or pre-positioning). The HBM is automatically transformed into a simplified model with a limited number of degrees of freedom that can be used in physics-based interactive simulation. Despite being simplified and interactive, the simulation can among others account for collisions between bones (to prevent penetration, limit range of motion, ...) and provide a rough transformation of the soft tissues. The PIPER framework aims to propose some alternative methodologies that can be used along current approaches. Positioning in PIPER typically starts with the Physics-based Interactive Pre-Positioning Module (or pre-positioning). The HBM is automatically transformed into a simplified model with a limited number of degrees of freedom that can be used in physics-based interactive simulation. Despite being simplified and interactive, the simulation can among others account for collisions between bones (to prevent penetration, limit range of motion, ...) and provide a rough transformation of the soft tissues.
  
-The pre-positioning process is the place where the user can input its various constraints, weight them, and compute a plausible posture (for the skeleton in particular). Constraints could also include a priori knowledge such as physiological observations or postural preferences which are not classical mechanical parameters. For now, physiological descriptions of the spinal curvature (called Spine controller) can interact with the model (e.g. collision detection on the vertebrae) during postural change.+The pre-positioning process is the place where the user can input its various constraints, weight them, and compute a plausible posture (for the skeleton in particular). Constraints could also include a priori knowledge such as physiological observations or postural preferences which are not classical mechanical parameters. For now, physiological descriptions of the spinal curvature (called the Spine predictor tool) can interact with the model (e.g. collision detection on the vertebrae) during postural change.
  
 Several options are then possible to transform the HBM using this pre-position as the target: Several options are then possible to transform the HBM using this pre-position as the target:
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 In all cases, the use of the Transformation smoothing after positioning was found to greatly improve the results. In some cases (for smaller motion), the pre-position may be directly used and lead to a plausible and runnable model after smoothing. In all cases, the use of the Transformation smoothing after positioning was found to greatly improve the results. In some cases (for smaller motion), the pre-position may be directly used and lead to a plausible and runnable model after smoothing.
  
-===== Foreword: where to find it? =====+{{ :ghbmc.png?nolink&300 |Interface in the preposition module (GHBMC, landmarks, frames)}} 
 +{{ :ghbmc_position.png?nolink&600 |example of posture change (Ircobi 2018)}} 
 +{{ :childpedestrian.png?nolink&300 |Example of custom affines on the child pedestrian model}} 
 + 
 +===== Where to find it? =====
  
   * see the [[downloads|download page]]   * see the [[downloads|download page]]
framework.1498559643.txt.gz · Last modified: 2017/06/27 12:34 by admin