算法检测形状? [英] Algorithm to detect a shape?

问题描述

嗨.

您知道用于检测图像形状的任何算法吗?

Hi.

Do you know any algorithms for detecting a shape in an image ?

thanks in advance.

推荐答案

数学形态学提供了更简单，更有效的形状检测工具.基本操作是扩张和侵蚀.

此 http://www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip-Morpholo.html [ http://www.dca.fee.unicamp. br/dipcourse/html-dip/c9/s4/front-page.html [

Mathematical morphology provides simpler and more efficient tools for detecting shapes and more. The fundamental operations are dilation and erosion.

This http://www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip-Morpholo.html[^] covers some of the basics (but it makes the concepts look harder than they are).

This one http://www.dca.fee.unicamp.br/dipcourse/html-dip/c9/s4/front-page.html[^] has some nice pictures, but is short on explanation.

If you could describe the task you''re trying to accomplish, maybe I could make some more specific recommendations.

假设这些是相对简单背景上的简单几何形状(如op摆在背景杂乱的真实物体上)，那么我建议:

1.确定哪些像素是前景(形状)和背景(其他所有像素)2.标识单个斑点(连接的一组相同类型的前景像素)
3.对于每个斑点，提取有意义的特征
4.根据提取的特征训练分类器
5.在新映像上测试系统
6.庆祝！

在第2步中，尝试使用洪水填充.
在第3步中，您可以尝试许多功能，例如周长与面积之比.
在第4步中，分类器可以是任何学习算法:神经网络，线性判别法等.


-Will Dwinnell
数据挖掘MATLAB

Assuming that these are simple geometric shapes on relatively simple backgrounds (as opposed to real objects against cluttered backgrounds), then I''d suggest:

1. Determine which pixels are foreground (shapes) and background (everything else)
2. Identify individual blobs (connected sets of foreground pixels of the same type)
3. For each blob, extract meaningful features
4. Train a classifier, based on the extracted features
5. Test the system on new images
6. Celebrate!

In step 2, try using a flood fill.
In step 3, there are many features one might try, such as perimeter-to-area ratio.
In step 4, the classifier could be any learning algorithm: neural network, linear discriminant, etc.


-Will Dwinnell
Data Mining in MATLAB

霍夫变换可以帮助您进行简单的形状检测，例如直线圆等.您最好看一下PDM(概率密度模型)，它们可以检测形状. PDM需要使用训练集进行初始化，然后再计算PCA. PDM不是那么困难..看看.

Hough transform can help you in simple shape detection like line circle etc.. You better look PDM(Probability density models ), these can detect shapes. PDM need to be initialized with a training set , after that it will calculate PCA . PDM is not that difficult.. have a look.

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