blocks|key|482405|text|具体细节取决于您所使用的语言，但通常它的工作原理如下：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|482406|以编程方式将每个图像视为一个二维数组。因此，要找出两个不同图像在同一像素(称为x，y)上的值的差异，您可以这样做：|482407|diff+=+image1x+-+image2x|482408|有一些实现细节你必须小心(例如，差异将被签名)，但这是基本的想法。|482409|如果您提到您正在使用哪种语言，我可以提供一些关于如何打开图像以便将其视为数组的建议。|482410|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|N|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|O|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|P|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|K|$]]

The particulars depend on which language you are using, but generally it works something like this:

Each image is treated programmatically as a two dimensional array. So to find the difference in values at the same pixel (call it x,y) for two different images you would do something like this:

diff = image1[x][y] - image2[x][y]

There are some implementations details you have to wary of (e.g. the difference will be signed), but this is the basic idea.

If you mention what language you're working with, I can offer some advice about how to open an image so that it can be treated like an array.

blocks|key|4263777|text|不要简单地将答案1中的差异相加，否则正负差异将被抵消！您需要对差值的平方或其绝对值求和。您还可以考虑将像素对相乘，并除以它们的和(相关性)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4263778|这三种方法现在在技术上都是正确的，但它们仍然远远没有实用价值。如果发现两张完全不相关的图像之间的差异比光照稍有不同的同一对象的两张图像要小，不要感到惊讶。|4263779|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Don't simply add the differences from Answer 1, or positive and negative differences will cancel out! You need to sum the squares of the differences, or their absolute values.
You could also consider multiplying the pairs of pixels and dividing by their sum (correlation). 

All three of these methods are now technically correct but they will still be far from practically useful. Don't be surprised to find smaller difference between two totally unrelated images than, say, two images of the same object with a slightly different illumination.

blocks|key|482478|text|“图像处理是如何完成的？”|type|unstyled|depth|inlineStyleRanges|entityRanges|data|482479|答案:这取决于你想要做什么处理。在图像处理中有数百种操作，每一种操作都可能有完全不同的方法，唯一的共同点是读取和存储像素数据。|482480|“我想要比较两张图像，找出它们之间的差异。我如何识别不同图像的两个拍摄像素之间的差异？”|482481|在问你图像的类型之前，我想问一下你所说的“识别两个拍摄像素之间的差异”是什么意思？您是否只想知道这两个图像是否不同？你想知道哪些像素是不同的吗？你想知道给定位置的像素是否不同吗？|482482|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

"How is image processing done?" 
Ans: Depends on what processing you want to do. There are hundreds of operations you could do in image processing, each one may have an entirely different approach, the only thing in common being reading and storing the pixel data.

"I want to compare two images and identify the differences between them. How do I identify difference between two taken pixels of different images?"

Before even asking you about the type of images - I would like to ask what you mean by "identify difference between two taken pixels"? Do you just want to know if the two images are different? Do you want to know which pixels are different? Do you want to know if pixels at a given location are different?

blocks|key|482534|text|试试processing.js，你可以用它做一些非常有趣的事情。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|482535|http://processingjs.org/|offset|length|482536|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|O|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

try processing.js you can do some really fun stuff with that. 

<a href="http://processingjs.org/" rel="nofollow">http://processingjs.org/</a>

blocks|key|4263800|text|随机选择像素并检查图像的大小。如果它们是相同的，那么您可以假定它们是相同的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4263801|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Random selecting pixels and check the size of the image. if they are the same, you could possible assume they are identical.

blocks|key|2797976|text|这是我几周前做的一个图像处理示例。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2797977|http://keshavsaharia.com/2011/03/17/theres-waldo-2/|offset|length|2797978|它是用Mathematica构建的，使用Where's+Waldo拼图，并使用相当简单的图像处理技术来找到Waldo。|2797979|图像处理是一个非常简单的概念。它几乎完全基于获取单个像素，并基于像素值相对于其他像素值或给定阈值的数值属性将变换应用于整个图像。|2797980|Mathematica是一个非常棒的图像处理工具，Matlab和Python也是如此(如果您正在寻找python解决方案，请查看PIL)。几乎每种编程语言都可以使用图像处理库--+C%2B%2B有EasyBMP，Java有各种各样的图像工具，而且每种语言都有自己的一套独特的方法。根据您的应用程序选择语言。|2797981|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|1F|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@$D|U|E|V|1|W]]|A|$]]|$1|F|3|G|5|6|7|X|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|Y|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|Z|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|M|$N|$5|O|P|Q|A|$R|C]]]]

Here's an image processing example that I did a couple weeks ago. 

<a href="http://keshavsaharia.com/2011/03/17/theres-waldo-2/" rel="nofollow">http://keshavsaharia.com/2011/03/17/theres-waldo-2/</a>

It's built in Mathematica, and takes a Where's Waldo puzzle and uses fairly simple image processing techniques to find Waldo.

Image processing is a pretty simple concept. It is almost entirely based around taking individual pixels and applying transformations to the image as a whole based on numeric properties of the pixel values in relation to other pixel values or to a given threshold value.

Mathematica is a fantastic tool for image processing, as is Matlab and Python (if you're looking for a python solution, check out PIL). Image processing libraries are available for pretty much every programming language - C++ has EasyBMP, Java has a wide variety of image tools, and every language has its own set of unique methodologies. Choose the language based on your application.

blocks|key|4263875|text|如果您只需要在像素的基础上对两个图像进行简单的比较，那么没有什么比计算两个图像的每个像素之间的差值总和更简单的了。这种方法不是很通用，例如，不同的照明可能会显着影响结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4263876|也就是说，图像比较仍然是一个开放的研究问题，它关系到图像挖掘的任务。|4263877|如果你需要一些更高级的图像比较，你可以尝试Rapidminer+with+IMMI扩展，它包含了几种高级的图像比较方法。|offset|length|4263878|entityMap|0|LINK|mutability|MUTABLE|url|http://spl.utko.feec.vutbr.cz/en/component/content/article/46-image-processing-extension-for-rapidminer-5^0|0|0|L|K|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@$F|S|G|T|1|U]]|A|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]]]

If you need just a simple comparison of two images on the pixel basis, there is nothing as simple as count sum of differences between each pixels of two images. This approach is not very versatile and for example different illumination can significantly affect results. 

What is to say, image comparison is still open research problem and it is related to the image-mining task.

If you need some more advanced image comparison, you can try <a href="http://spl.utko.feec.vutbr.cz/en/component/content/article/46-image-processing-extension-for-rapidminer-5" rel="nofollow">Rapidminer with IMMI</a> extension which includes several advanced methods for image comparison.

blocks|key|4263923|text|图像处理是一个庞大的领域，所以我不认为这里的任何一个答案都能教会你关于图像处理的所有知识。在互联网上有很多很棒的资源和讲座，你可以从中学到大量的信息。如果你想要一本关于信号处理的好书(信号几乎代表了任何数据类型:光、声音、图像等……)其中涵盖了大量的信号处理技术，请查看Signals+and+Systems。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4263924|但是，如果你只想学习可应用于图像的基本过程，请查看processing+language+(如jaboston所说)及其教程/学习书籍，因为它本质上是为视觉艺术家设计的，并且使图像处理比其他语言更省力(从语法上讲)：http://processing.org/。|4263925|而erich有权使用欧几里德距离来计算两个像素之间的差异。|4263926|entityMap|0|LINK|mutability|MUTABLE|url|https://rads.stackoverflow.com/amzn/click/com/0138147574|1|http://processing.org/^0|3R|J|0|0|30|M|1|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]]|C|$]]|$1|D|3|E|5|6|7|V|8|@]|9|@$A|W|B|X|1|Y]]|C|$]]|$1|F|3|G|5|6|7|Z|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|10|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]|P|$5|K|L|M|C|$N|Q]]]]

Image processing is a massive field so I don't think any single answer on here will teach you everything there is to know about image processing. There are many great sources and lectures scattered all over the internet that you can learn tons of information from. If you want a good book for signal processing (signal is representative of almost any datatype: light, sound, image, etc, etc...) which covers a huge number of signal processing techniques check out <a href="https://rads.stackoverflow.com/amzn/click/com/0138147574" rel="nofollow noreferrer" rel="nofollow noreferrer">Signals and Systems</a>.

But otherwise if you just want to learn basic processes that can be applied to images check out the processing language (like jaboston said) and its tutorials/learning books as its essentially designed for visual artists and makes image processing a much less laborious (syntactically) task than other languages: <a href="http://processing.org/" rel="nofollow noreferrer">http://processing.org/</a> .

And erich has the right of it- use the euclidean distance to work out the difference between two pixels.

blocks|key|4263959|text|图像相似性并不是一件容易的事情，仍然是一个有待解决的研究问题。逐个像素的比较通常不会给出好的结果，同时它非常耗时。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4263960|其中一种方法是使用Rapidminer+(开源工具)。Radpiminer+(广泛使用的数据挖掘平台，http://rapid-i.com)和IMMI插件(IMage+mining+extension，http://splab.cz/immi)实现了类似的方法，并且可以进行一些非平凡的相似性度量。|offset|length|4263961|entityMap|0|LINK|mutability|MUTABLE|url|http://rapid-i.com/|1|http://splab.cz/immi^0|0|1F|I|0|2T|K|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@]|9|@$D|R|E|S|1|T]|$D|U|E|V|1|W]]|A|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]|N|$5|I|J|K|A|$L|O]]]]

Image similarity is not an easy task and is still open research problem. Pixel per pixel comparison usually does not give good results and at the same time it is quite time consuming.

One of the approach is to use Rapidminer (open-source tool). Radpiminer (widely used data mining platform, <a href="http://rapid-i.com" rel="nofollow">http://rapid-i.com</a>) with IMMI plugin (IMage MIning extension, <a href="http://splab.cz/immi" rel="nofollow">http://splab.cz/immi</a>) implements similar methods and can do some non-trivial similarity measurements.

How is image processing done?

I want to compare two images and identify the differences between them. How do I identify difference between two taken pixels of different images?

image processing

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

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图像处理是如何完成的？我想要比较两个图像，并确定它们之间的差异。如何识别不同图像的两个拍摄像素之间的差异？

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