SIFT 在 OpenCV 中的参考图像中找不到任何特征
我有一个目标徽标的图像，我试图用它来查找其他图像中的目标徽标。我目前正在运行两种不同的检测算法来帮助我检测图像上的任何徽标。我使用的第一个检测算法是基于直方图的，我在图像中搜索屏幕上颜色非常相似的一般区域。从那里我运行 SIFT 以进一步获取我正在寻找的对象。这适用于大多数徽标，但是我拥有的目标徽标甚至没有拾取徽标中的关键点。
我想知道是否可以做些什么来帮助找到图像中的一些关键点。非常感谢任何建议。
下面是 SIFT 未选取的图像：
提前致谢。
编辑 我尝试使用 Julien 的想法根据模型的不同尺度和旋转进行模板匹配，但仍然收效甚微。我已经包含了我想要测试的图像。
I have an image of the target logo that I am trying to use to find target logos in other images. I am currently running two different detection algorithms to help me detect any logos on the image. The first detection algorithm I use is Histogram based in which I search the image for a general area on screen where the colors are very similar. From there I run SIFT to further get the object that I am looking for. This works on most logos however the Target logo that I have isn't even picking up and keypoints in the logo.
I was wondering if there was anything I could do to help locate some keypoints in the image. Any advice is greatly appreciated.
Below is the image that isn't being picked up by SIFT:
Thanks in advance.
EDIT
I tried using Julien's idea for template matching based on different scales and rotations of the model, but still got little results. I have included an image that I am trying to test against.
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评论（3）
你的图像中没有关键点...
为什么？
如果您正在搜索没有大变化（旋转、平移、噪声等）的徽标，您可以尝试使用模板匹配方法）一个简单的相关性是最容易的。
如果你想更进一步，我的想法之一，我从未实现过，但可能很有趣：将是拥有一组可以缩放、旋转、扭曲、去饱和、使用函数增加噪声的图像，然后应用模板匹配使用您从以前的模板中获得的这组图像......
这个想法来自 SIFT 和小波变换，其中我们使用以某种方式（旋转、噪声、频率等）改变的函数，以便使我们的变换针对任何图像中发生的这些基本变化具有鲁棒性您想要“检查”的内容。
这对你来说可能是一个主意！
这是一张总结我想法的图像，您旋转和缩放模板，实际上它会创建一个新的旋转/您可以尝试匹配的缩放模板，它将提高鲁棒性（即使如果您选择要更改很多参数，它可能会很长）。好吧，我并不是说这是一种算法，但这可能是一个有趣且非常基本的尝试想法......
朱利安，
There is no keypoint in your image...
Why ?
What you could try is a template matching method if you are searching for this logo without big changes (rotation, translation, noise etc) a simple correlation is the easiiiiest.
If you want to go further, one of my idea, that I have never implemented but which could be funny : would be to have sets of this image that you scale, rotate, warp, desaturate, increase noise with functions and then apply template matching with this set of images you got from your former template...
Well this idea comes from SIFT and Wavelet transform, where we use sort of functions that we change in some ways (rotation, noise, frequency etc...) in order to give robustness to our transform against these basic changes that occur in any image that you want to "inspect".
That could be an idea for you !
Here is an image summarizing my idea, you rotate and scale your template, actually it creates a new rotated/scaled template that you can try to match, it will increase robustness (even if it can be very long if you choose a lot of parameters to change). Well i'm not saying that's an algorithm, but it could be a funny and very basic idea to try...
Julien,
这个标志在特征匹配上存在问题还有一个原因。对于没有任何平滑度的人造图像，大多数功能都表现不佳。所有导数都是 1 像素大小，并且特征检测器依赖于导数。您必须稍微平滑图像。 Ofcorse 对于这个特定的标志来说，由于高度对称，它不会有帮助。您可以使用霍夫变换来检测圆内的圆。与模板匹配相比，它会给你更好的结果。
There is another reason that this logo is problematic for feature matching. Most features work pretty bad with artificial images that doesn't have any smoothness. All the derivatives are exactly 1 pixel size and features detector rely on derivatives. You have to smooth the image a bit. Ofcorse for this specific logo it will not help due to high symmetry. You can use hough transform to detect circles inside circles. It would give you better results in comparison with template matching.
我认为您可以尝试使用 MSER 功能 - https://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
看一个例子：
https://www.mathworks.com/examples/matlab-computer-vision/mw/vision_product-TextDetectionExample-automatically-detect-and-recognize-text-in-natural-images
I think you can try using MSER features- https://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
See an example:
https://www.mathworks.com/examples/matlab-computer-vision/mw/vision_product-TextDetectionExample-automatically-detect-and-recognize-text-in-natural-images