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Residue Filtering

The implementation incorporates a filter component which carves out shape residue based on intuition and not a clear description of a method elsewhere, e.g. in the IJCV paper (there is none specified in the paper, just the observation that it's essential). They appear to be using a hybrid of tricks to achieve this, so we start with threshold that is permissive enough to accept natural variation. Next, a special case to remove border differences will be implemented. Figure shows a real example as it is being processed in phase 1.

In order to improve models that are being built automatically, i.e. without human intervention, effective removal of noise and outliers is done with spatial thresholds (depth and boundaries perpendicular to the camera). There are more examples in Figure . These are examples of outlier stripping in the shape residue, without further cleaning of differences near face borders.

Figure: Process of cleaning up the residual of two images (GIP data)

Figure: Examples of residual images before and after outliers removal