Mutation 45

We're going to switch gears for awhile and use the original self portrait stack as opposed to the spatially normalized self portrait stack. This portrait was generated with a paint action sequence that auto draws the eyes and mouth of every portrait in the input stack.

So it's an example of using a repetitive process encoded in a PASeq. All of the processing is done automatically, including the face feature detection. The diversity of the original portrait set is desired for this kind of processing (as opposed to the set of previous examples where it was desirable for the painted facial features to be spatially normalized.

Mutation 44

This portrait was generated by stack filtering the spatially normalized set of self portraits with the temporal difference1 filter.

Mutation 43

This portrait was generated by processing the spatially normalized self portraits with a temporal rank neighborhood filter.

Mutation 42

This portrait was generated by stack filtering the spatially aligned self portraits with a different temporal rank neighborhood filter preset.

Mutation 41

This portrait was generated by stack filtering the spatially normalized self portraits with the temporal rank neighborhood filter.

Mutation 40

This portrait was generated by stack filtering the spatially normalized slef portraits with the temporal scan tracker 2 smart seam processing using a horizontal scan and a wide temporal processing window.

Mutation 39

This portrait was generated by stack filtering the spatially normalized self portraits with the temporal scan tracker 2 smart seam processing, using a vertical scan direction.