以编程方式为各种面板实施引导视图技术
我刚刚读了 Scott Hansleman 的文章 漫画中的引导视图技术 我认为如果在其他途径（特别是在 manga 中实现的话，这会很棒）
我的意思是，从右到左阅读本身可能有点奇怪，这会降低新读者的进入门槛。
我想知道是否可能有一个开放的源项目在野外，或者如果没有，那么可能是一种开始类似这样的事情的方法，因为我不是图像处理专家。特别是我真的需要弄清楚哪些线是面板以及在哪里切片成更小的图片。因为漫画对于线条粗细都有自己的偏好，所以我不确定是否有一种简单的方法可以跨许多不同的边框粗细和样式进行此操作。语言在这里并不重要，我真正关心的是处理概念和攻击模式。
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.
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您可以首先查看直线霍夫变换的 Duda-Hart 实现。
http://en.wikipedia.org/wiki/Hough_transform
霍夫算法将产生直线方程线。您可以从中找到交点、识别矩形等。
您还可以使用基于内核的角点检测来查找 T、L 和 X 交点。
面板
一个困难是漫画中的某些 不会有“硬”边缘，或者可能有波浪形、圆形/椭圆形、法式曲线等边缘。您可以找到针对特定问题的特定算法，但很难将这些算法概括为一组规则和适用于所有（甚至大多数）样本的编程逻辑。看来一部优秀漫画的标志就是优雅且有时令人惊讶的分版，“令人惊讶”是不可预测的同义词。尽管有很多方法可以将图像“分割”成不同的区域，但这仍然是一个活跃的研究领域。
但如果您从霍夫线开始，您将会有一个良好的开端，并学到很多有关图像处理的知识。
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.