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 3

Thought i'd try an experiment with eye detection. The stack of portraits are processed with a PASeq that auto-selects the painted eyes and then paints those into the canvas. So the eyes build up over time.

So this one is a little different than the stack filter effects that use temporal image processing. Building a PASeq that processes a stack of images to build up a single canvas can be thought of as a different approach to stack filtering. We'll get back to time-based processing tomorrow, but it's important to realize that there's many different approaches you can take to build stack filtering effects.

Again, i'm using the original series of 2010 Siggraph portrait virus images for this example. We will be discussing building a source image series spatially warped to a standard image. If we had used that sequence for this particular effect all of the eyes would be in the same position, so it would not be as effective. However, for other kinds of time based processing the standardized sequence can be very useful. We will show an example of that tomorrow.