为什么不变矩对于同一图像略微不同 [英] why Invariant moments are slightly diffident for the same image

问题描述

它被提及为胡矩从尺度，旋转和方向不变。但是当我通过使用不同大小和角度的相同图像来比较Hu时刻时，我的值略有不同。



i获得100,75（高度，宽度）的图像）第一次和第二次相同的图像200,150（高度，宽度）。



有人可以解释原因吗？ PLZ帮助。



谢谢你

解决方案

请看我对这个问题的评论。想象一下，你所有的计算都是正确的，这很可能。这可能只是方法的准确性。但是，如果你重新采样一些图像，那么不是完全相同的图像。用这种方式想一想：你的图像本身是用像素值表示的，只是一些理想场景的近似值。颜色被破坏，一些可能饱和或成为价值离散的主体。但对我们来说更重要的是，像素本身并不存在于自然界中：每个像素的颜色都是基于一些平均值来反映来自某个3D角度的光，并且还受到光学系统像差的影响;而且，作为电子设备的像素不是独立的，它们通过一些寄生效应略微相互影响。但是来自相机的是单点真相。当您在软件中重新采样和旋转图像时会发生什么？像素之间的原始相关性主要被破坏，并且基本上引入了额外的离散化误差。当您升级图像时（尤其是需要一些固有的质量损失），这一点尤其明显。如果您阅读了算法（或者即使您不想，只是想一想），您将看到一些不存在的像素必须使用一个或另一个外推算法创建 。这种算法总是有一些有限的准确性。换句话说，你创建了一些新的图像，只是与原始图像非常相似（如果这是一个高放大率，则不会如此接近）。这个图像的所有功能都有些不同，包括它的时刻。

-SA

it is mentioned as Hu-moments are invariant from scale, rotation and orientation. But when i compared the Hu-moments by using same image with different sizes and angles, i got slightly different values.

i get image with 100,75(height,width) at first time and then same image with 200,150(height, width) at second time.

can someone explain the reason for that? plz help.

thank you

解决方案

Please see my comment to the question. Imagine all your calculations are correct, which is pretty likely. It could be just withing the accuracy of the method. But then, if you re-sample some image, it is not exactly the same image. Think of this in this way: you image itself isexpressed in pixel values, is only an approximation of some "ideal scene". Colors are mangled, some may be saturated or be a subject of value discretization. But what is more important to us, the pixels themselves do not exist in the nature: each pixels gets its color based on some averaged value reflecting the light coming from some 3D angle, and also mangled by optical system aberrations; and also, pixels as electronic devices are not independent, they slightly affect each other through some parasitic effects. But what comes from the camera is the single point of truth. What happens when you re-sample and rotate the image in software? Original correlations between pixels are majorly destroyed, and, essentially, you introduce additional discretization error. It will be especially apparent when you upscale the image (which always entails some inherent quality loss). If you read about the algorithm (or even if you won''t but just think about it), you will see that some non-existent pixels have to be "created" using one or another extrapolation algorithms. Such algorithms always have some limited accuracy. In other words, you create some new image, only closely resembling the original (or not even so closely, if this is a high magnification). And this image is somewhat different in all its features, including its moments.

—SA

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