使用静脉系统的叶标识符 [英] Leaf identifier using vein system
问题描述
我需要帮助一些人。我正在进行最后的研究。
我正在计划使用静脉系统做叶标识符。所以我不想使用神经网络。因为它太难了。所以朋友是你知道某种技术可以用于我的项目吗？
就像指纹识别一样。
用于指纹验证的C＃框架 [ ^ ]
所以事情需要像这样工作。
首先我通过浏览为系统提供图像。之后需要进行灰度转换。之后需要比较我的数据库图像和浏览图像。
请各位帮帮我吧。
如果这是你的最终研究，那么你的研究就好了。基于静脉的识别是一种新兴的生物识别方法，它并不像你起草的那么简单：它不仅仅是图像比较的问题。首先，有几个主要目标，如手掌，手指甚至全身识别。一方面，静脉系统不是平面的（它是一个3D物体），它不是恒定的而不是静态的;另一方面，您必须考虑用于捕获图像的技术和设备。
但是，因为指纹识别是基于捕获模式的编码（更准确地说是它的特定和特征方面），并且比较不是图像的比较，而是这些代码的比较 - 静脉模式识别具有相同的基本思想。但由于指纹识别是一种古老而且记录良好的科学，静脉模式识别是新的。
这可能是一个简单的方法：
假设您有图像（A）。
进行直方图校正以增强静脉能见度B = Hc（A）
找到静脉的颜色范围并抑制所有其他C = Cr（B）
对增强图像上的静脉进行形态骨架检测D = S（B）
使用A和B制作骨架的矢量化版本E = V（B，D），减少点设置
现在你有很多行。
这是最简单的部分，因为你需要找出一种编码技术。这是我的0级建议：
存储每条线，它的极坐标位于手的周边，以及周围的其他线。这可以是编码模式。
现在，当你匹配这两种模式时，你不会使用离散方法。使用一些并行方法来找到最匹配的候选者。给匹配线的数量，匹配线的长度偏差，匹配线的坐标偏差赋予权重。所以你可以获得整体认可的排名。值越高，识别越好。
当然，这可以大大改进，所以不要浪费你的时间在这个主题上寻找出版物。希望你能从大学图书馆得到它们......
AForge [ ^ ]可以为您提供很好的帮助你
i need some help guys. i''m doing my final research.
i''m planing to do Leaf identifier using vein system.so i don''t want to use neural network.Because its too hard.so friend are u know some kind of techniqs for use my project?
its like finger print recognition.
A Framework in C# for Fingerprint Verification[^]
so things need to work like this.
first i give image for the system by browsing.after it need to do some grey scale conversion.after that it need to compare my data base image and browsed image.
please guys help me for this.
If this is your "final research", than do your research. Vein based identification is an emerging approach in biometric identification, and it is not as simple as you drafted: it''s not just a matter of image comparison. First of all, there are several major targets, like palm, finger and even full-body based identification. On one hand the vein system is not planar (it is a 3D object), it is not constant and not static; on the other hand you have to consider the technique and the device you are using to capture the images.
But as the fingerprint recognition is based on a coding of the captured pattern (more precisely of it''s specific and characteristic aspects), and the comparison is not a comparison of images but of these codes - the vein pattern recognition has just the same basic idea. But as fingerprint recognition is an old and well documented science, vein pattern recognition is new.
This could be a simple approach:
Let''s suppose you have an image (A) .
Do a histogram correction to enhance vein visibility B=Hc(A)
Find the "color range" of the veins and suppress all other C=Cr(B)
Make a morphological skeleton detection for the veins on the enhanced image D=S(B)
Using A and B make a vectorized version of the skeleton E=V(B,D), with reduced point set
Now you have a bunch of lines.
That was the easiest part, since you need to figure out a coding technique. This is my level 0 suggestion:
Store every line with it''s polar coordinate from the perimeter of the hand, and from the other lines around it. This can be the coded pattern.
Now, when you match the two patterns, you ca''t use a discrete approach. Use some parallel approach to find the best matching candidates. Give a weight to the number of matched lines, the deviation of length of the matched lines, the deviation of the coordinates of the matched lines. So you can get a rank of the overall recognition. The higher the value, the better the recognition.
Of course, this can be refined considerably, so don''t waste your time and look for publications in this topic. And hope you can get them from the university library...
AForge[^] can be a great help for you
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