将图像与噪声模板匹配的最佳方法是什么？ [英] What is the best method to template match a image with noise?

问题描述

我有一个大图像（5400x3600），我需要检测多个CCTV。

I have a large image (5400x3600) that has multiple CCTVs that I need to detect.

检测需要花费大量时间（4-7分钟） 。但它仍然无法解决某些闭路电视。

The detection takes lot of time (4-7 minutes) with rotation. But it still fails to resolve certain CCTVs.

匹配这样的模板的最佳方法是什么？

What is the best method to match a template like this?

我正在使用skImage - openCV对我来说不是一个选项，但我也对此提出建议。

I am using skImage - openCV is not an option for me, but I am open to suggestions on that too.

例如：在下面的图片中，模板是与第二张图像正确匹配 - 但第一张图片不匹配 - 我想由于文字BLDG ...所产生的噪音

For example: in the images below, the template is correct matched with the second image - but the first image is not matched - I guess due to the noise created by the text "BLDG..."

推荐答案

最快的方法可能是一系列提升的分类器，这些分类器经过多种徽标和可能的变体训练y一些轮换和一些负面的例子（非标志）。您必须粗略地缩放整体图像，以便测试和训练示例大致按比例匹配。与花费大量时间搜索兴趣点并为学习和搜索创建描述符的SIFT或SURF不同，二元分类器将大部分负担转移到训练阶段，而您的测试或搜索将更快。

The fastest method is probably a cascade of boosted classifiers trained with several variations of your logo and possibly a few rotations and some negative examples too (non-logos). You have to roughly scale your overall image so the test and training examples are approximately matched by scale. Unlike SIFT or SURF that spend a lot of time in searching for interest points and creating descriptors for both learning and searching, binary classifiers shift most of the burden to a training stage while your testing or search will be much faster.

简而言之，级联运行的方式是第一次测试会丢弃大部分图像。如果第一次测试通过，其他测试将遵循并改进。它们将是超快速的，仅包括每个点周围的平均强度比较。只有少数几个位置可以通过整个级联，并且可以通过其他测试进行验证，例如旋转关联例程。

In short, the cascade would run in such a way that a very first test would discard a large portion of the image. If the first test passes the others will follow and refine. They will be super fast consisting of just a few intensity comparison in average around each point. Only a few locations will pass the whole cascade and can be verified with additional tests such as your rotation-correlation routine.

因此，分类器是有效的，不仅因为它们可以快速检测到您的对象，还因为它们也可以快速丢弃非对象区域。要阅读有关增强分类器的更多信息，请参阅以下openCV 部分 。

Thus, the classifiers are effective not only because they quickly detect your object but because they can also quickly discard non-object areas. To read more about boosted classifiers see a following openCV section.

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