Mutation 37

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

Mutation 36

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

Mutation 48

This portrait was generated by processing the original set of self portraits with the temporal motion rank max filter.

Mutation 6

This image uses another temporal motion rank filter, but it's choosing the pixel with the minimum motion for each spatial location in the stack of portraits. So it's related to yesterday's post, but kind of the inverse effect. It accents similarity as opposed to difference in adjacent images in the portrait stack.

Mutation 5

This image was generated by using the temporal motion rank filter with the spatially normalized stack or portrait virus paintings. So it analyzes the stack of images and displays the pixel with the maximum motion at each spatial location in the output canvas. Obviously the ordering of the images in the stack is going to influence the result of this kind of processing. So by shuffling the ordering of the images in the stack sequence you could generate different variations of the effect.