Implementing guided view technology programmatically for a variety of panels

Question

I just read Scott Hansleman's post on Guided View Technology in comics and I though that this would be awesome if implemented in other avenues (specifically in manga )

I mean reading right to left in itself can be a little weird to start with and this would lower the barrier to entry for new readers.

I was wondering if there was possibly an open source project out there in the wild or if not then possibly a means to get started with something like this as I am not an image processing guru. In particular I just really would need to figure out which lines are panels and where to slice into smaller pictures. Because comics all have their own prefs as far as line thickness I'm not sure if there is a simple way to do this that works across many different border thicknesses and styles. Language doesn't matter so much here, I'm really about dealing with concepts and patterns of attack.

Answer 1

You can start by looking at the Duda-Hart implementation of the Hough transform for lines. http://en.wikipedia.org/wiki/Hough_transform

The Hough algorithm will yield equations for straight lines. From that you can find intersections, identify rectangles, etc.

You can also use a kernel-based corner detection to find T-, L-, and X-intersections. http://en.wikipedia.org/wiki/Corner_detection

One difficulty is that some panels in comics won't have "hard" edges, or may have edges that are squiggly, circular/elliptical, French curvy, etc. You can find particular algorithms for particular problems, but it would be hard to generalize these algorithms in a set of rules and programmatic logic that will work for all (or even most) samples. It seems that a hallmark of a good comic could be considered to be the elegant and sometimes surprising panelization, "surprising" being a synonym for unpredictable. Although there are many methods to "segment" an image into different regions, this is still an active area of research.

But if you start with Hough lines you'll have a good start and learn a lot about image processing.