如何使用MSER检测图像区域 [英] How to use MSER to detect regions in images

问题描述

我已经创建了一个提取在的MSER数据，并将其存储应用CvSeq * 。 ，我想知道是否有任何功能或教程OpenCV的，我可以用用两个图像的提取的数据与其他图像的数据进行比较。

I have created an application that extracts the MSER data and stores it in a CvSeq*. I was wondering if there were any functions, or tutorials, in OpenCV that I could use to compare the data with another image using the extracted data of both images.

感谢。

推荐答案

MSER的最简单的实现恰好是的这个另一个则是使用C API。有来自谷歌的SoC <一个又一个上市href=\"http://$c$c.google.com/p/opencv-feature-tracker/source/browse/MSER.cxx?r=e4bfa468b10c17de22785a464d8636da83b1e35a&spec=svn9427a685b9331180e64adfe1b92916b3b571a96c\"相对=nofollow>此处使用C ++ API。

The simplest implementation of MSER happens to be this one using the C API. There's another listing from the Google SoC here using the C++ API.

我想以比较结果，将是实现code以上任何环节的最佳途径。用Matlab效果比较一般是一件好事，因为我们可以期望这是一个标准的（或多或少）。 VlFeat 具有与具有MSER功能C和Matlab的接口库。最后一个环节也有从那里你或许能理解这数据来比较的简要说明。什么样的比较做你的想法 - 如果它在两个不同的图像区域之间的相似性，然后利用区域的灰度级共生矩阵（GLCM）应该工作。该MSER会给你的地区，但比较可能不需要MSER的进一步的数据。

I guess your best way to compare results would be to implement the code in any of the above links. Comparing the results with Matlab is generally a good thing, as we can expect that to be a standard (more or less). VlFeat has a library with both C and Matlab interfaces that has MSER functions. The last link also has a brief explanation from where you might be able to understand which "data" to compare. What sort of comparison do you have in mind - if it's similarity between regions in two different images, then using a Gray level Co-occurrence Matrix (GLCM) of the regions should work. The MSER will give you the regions, but comparison may not require further data of MSER.

你是用OpenCV的 cvMSER（）功能顺便说一句，或code整个事情？

Did you use the OpenCV cvMSER() function btw, or code the entire thing?

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