Segmentation in Relation to Models and NRR
Date: Friday, July 01 @ 11:16:59 BST
Topic: Technical Notes

The following observations motivate work towards the inclusion of segmentation in the process of automatic (appearance) model-building:

  • Registration aims at getting segments to overlap
  • Maybe segment one image and propagate to all others via model
  • Use segmentation to improve evaluation (similar to label)
  • Conversely, improve segmentation using dynamics of models (built using NRR)
  • Look at many images (sequence) of modelled images, project back from model to get segmentation
  • The approach gives registration, model, and segmentation directly from the raw data
  • Segmentation is arbitrary (not rational but data-driven), just like choice of landmark points
  • Create small compartmentalised models of constituent segments
  • When model is built, see variances in mean and optimise points, then re-run model-building
  • This approach can progressively improve identification of landmarks
  • Hence, better models can be built automatically
  • Expensive approach as models are built over and over again to get refined

[From the May 31st 2005 Progress Report]

This article comes from MARS - Models of Appearance, Registration and Segmentation

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