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Segmentation in Relation to Models and NRR
Posted on Friday, July 01 @ 11:16:59 BST by admin
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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]
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