我的用户吸引他们的签名在我的应用程序中使用的触摸事件，并把它转换成位图。我想提取每个签名的独一无二的规格和存储在主服务器的规格进行比较。

My users draw their signature in my application using touch events and I convert it to a bitmap. I want to extract unique specifications of each signature and compare it by its specifications stored in main server.

我怎么能这样做？

什么是主，企业提取算法签名的独特功能？

What is the main and enterprise algorithm for extracting unique features of a signature?

在此先感谢，

推荐答案

我不是计算机视觉专家，但作为一个多数民众赞成涉足研究生，这听起来像功能苛求就是你要寻找的。我想到的第一件事是SIFT（尺度不变特征转换）。这个算法将允许你的是原件的存储特征相比较的副本，并确定一个匹配的精度的一个高层次，即使在样品被缩放或旋转的情况下。

I'm not a computer vision expert, but as a grad student that's dabbled, it sounds like feature exaction is what you're looking for. The first thing that comes to mind is SIFT (Scale-Invariant Feature Transform). This algorithm would allow you to compare stored features of an original signature to copies and identify a match with a high level of accuracy, even in the cases that the sample is scaled or rotated.

通过一个简单的谷歌搜索，你可以找到一些学术论文，例如this 之一或这一是专门与使用的算法的签名/生物识别技术。

With a simple Google search you can find a number of scholarly papers, for example this one or this one that are specifically related to using the algorithm for signatures/biometrics.

有没有这使用的OpenCV与分析筛选的堆栈溢出文章。

如果有一些我不知道的是特别适合于这个问题，我鼓励社会附和更好的视觉算法。

If there are better vision algorithms that I'm unaware of that are particularly well suited to this problem I encourage the community to chime in.