使用MATLAB的对象识别 [英] Object recognition using MATLAB

问题描述

您有关于使用MATLAB实现2D对象识别的任何想法吗？

Have you any idea about implementing 2D object recognition with MATLAB? Which characteristics of objects can feed a neural network?

推荐答案

对象识别通常是所有图像处理任务的主要部分之一。从简单案例，如指纹识别和最佳字符识别到移动跟踪等。

Object Recognition is generally one of the main part of all image processing task. From Simple cases, like fingerprint recognition and Optimal Character Recognition to movement tracking and etc.

当然有很多不同的方法，问题。它只能基于颜色（颜色基础对象识别），说有一个红色的球一个绿色的字段，所以只有通过检测绿色，你可以识别球喜欢这里跟踪球！另一种简单的方法是形态运算符。此外，可以使用直方图和像素的分布，找到图片的所有边 like here 。此方法也用于查找书写文本的基线。

Of course there are many different approaches, considering given problem. It can be only based on color (color base object recogniton), say there is a red ball in a green field so only by detecting green color you can identify the ball like here for tracking ball!. The other simple approach is Morphological Operator. Furthermore, one can uses histogram and from distribution of pixels, find all edges of the picture like here. This method is also used to find the baseline of the written text as well.

更高级的方法基于机器学习方法。 神经网络是最知名的，它基本上通过一系列示例来训练您的模型，找到适当的权重/值为神经元，最后要求模型判断新的例子（测试）。当然，向网络提交图像不是真的明智;除了计算方面，还有过拟合问题。因此，提取图片中的常见图案是另一个挑战。说，所有字符A所遵循的一些模式可以是曲线，角度，强度，FT值，并将其与L区分开来，等等。此部分也称为尺寸缩小，因为您将所有图片像素映射/组成多个数据点。 PCA（主要成分分析），并检查 PCA 和 SVD 。这些方法仅在一些最高变体基础上解释数据的变化。

More advanced methods are based on Machine Learning Approach. Neural network is most known which basically you train your model by bunch of example, find proper weights/values for neurons and finally asking the model to judge about the new example (test). Of course submitting an image to the network is not really wise; Apart from the computational aspects, there is an over fitting issue. So extracting common pattern among pictures is another challenge. Say, some pattern that all characters "A" are following, could be the curve, angles, intensity, FT values and distinguish it from "L" and so on. This part is also called as Dimension Reduction, since you are mapping/composing all picture pixels into several data point. PCA (principle component analysis) and also check the PCA and SVD in matlab. These methods explain variation of data only in some most high variant basis.

机器学习的另一个观点，这些日子更热在统计方法中，通过查看在对象作为信号和一切作为噪声。 高斯过程和内核方法放置在这一边。只需在Google中的对象跟踪旁边搜索这些术语即可获取数千篇学术论文。

The other point of view of Machine Learning which these days is more hot Is in Statistical approach, by looking at the object as the signal and everything else as noise. Gaussian Process and Kernel Methods are placed in this side. Just search these terms beside "object tracking in Google to get thousands academic paper!.

总之，它真的一般性问题;根据问题，解决方案可以更改。我建议您查看 http://stats.stackexchange.com/ ，因为您的问题可以适合这些人的专业知识。

In sum, its really general question; Depends on the problem, solution can be changed. I suggest you to look through http://stats.stackexchange.com/ since your question can fit to those guys expertise.

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