如何执行逐像素匹配以检查模板数据库中是否存在查询图像 [英] How to perform pixel-by-pixel matching to check whether a query image exist in the template database or not

问题描述

我正在研究背手静脉识别系统。我已经对图像进行了二值化和预处理，然后进行了细化静脉图案的特征提取（白色像素坐标），如下图所示（图1）。对10个图像重复这些步骤，并将它们的坐标存储在.txt文件中。

I am working on dorsal hand vein recognition system. I have already binarised and pre-processed the image followed by feature extraction (white pixels coordinates) of the thinned vein patterns as shown below in the figure (Image 1). These steps were repeated for 10 images and having their coordinates stored in .txt file.

现在，假设我有一个查询图像（图像2），如下所示，上面提到的所有步骤都已应用并检索了坐标。

Now, let's say I have a query image (Image 2) as below where all the mentioned above steps have been applied and coordinates were retrieved.

匹配目的，我想调整这个纸张匹配策略，其中指出为了在二进制图像之间进行相似性匹配，以某种方式实现完全相同的算法。匹配是双向处理。在第一步中，算法扫描查询图像并获取每个前景像素（也可以采用背景像素）值，并将其与相应位置的数据库图像中的像素值进行比较。如果在数据库图像中的相同位置找到相同的值，则他将被视为命中数。否则，将其视为未命中计数，并且最后将命中和未命中计数的差除以查询图像中的前景像素的总数。此除法的结果给出一个数字，表示查询图像与数据库图像（SQD）的相似程度。在第二步中，扫描数据库图像，并将其前景像素元素与查询图像进行比较，如第一步中所做的那样。这将给我们一个结果，指示数据库图像与查询图像（SDQ）的相似性
。然后将SQD和SDQ的平均值（平均
相似性度量（ASM））作为检索过程的排名度量。

For the matching purpose, I want to adapt this paper matching strategy which stated that "An algorithm that, somehow, does the exact same thing is implemented in order to do a similarity matching between binary images. The matching is a two way process. In the first step, the algorithm scans through the query image and takes every foreground pixel (background pixels can also be taken) value and compares this with the pixel value in the database image at the corresponding location. If it finds the same value at the same position in the database image, this will be taken as a hit count. Otherwise, it will be taken as a miss count and finally the difference of the hit and the miss count is divided by the total number of foreground pixels in the query image. The result of this division gives a number that indicates how Similar the Query image is to the Database image (SQD). In the second step, the database image is scanned and its foreground pixel elements are compared against the query image as is done in the first step. This will give us a result that indicates how Similar the Database image is to the Query image (SDQ). Then the average of the SQD and the SDQ, Average Similarity Measure (ASM), is taken as a ranking measure for the retrieval process."

谢谢。

推荐答案

像素逐像素很容易，你只需散列图像并存储有，然后哈希新图像并比较哈希。但如果缩放图像，或者在有损压缩的情况下重新保存，则会失败。

Pixel-by-pixel is easy, you just hash the image and store the has, then hash the new image and compare the hashes. But that is going to fail if images are scaled, or in case of lossy compressions, re-saved.

这将基本匹配如果有人上传了同一个文件两次。这可能是也可能不是你所追求的。

That's going to essentially match if somebody uploads the same file twice. This may or may not be what you are after.

如果没有，你需要某种图像相似度算法。有一个问题关于这一点，在这里。图像比较 - 快速算法

If not, you need some sort of image similarity algorithm. There is a question about that already, here. Image comparison - fast algorithm

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