问题标签 [image-segmentation]

我必须在 matlab 中编写这个函数来根据欧几里得或马氏距离对图像进行颜色分割。您传入欧几里得的训练数据、mahalobis 的训练数据和 cov 矩阵。

我在这里得到了理论和数学以及如何在 matlab 中编写函数，但我不知道如何分割图像 RGB 并做到这一点。谁能指出我正确的方向？不允许循环。

谢谢

I have a task where I need to segment a web page visually so that I may be able to calculate the distance between two segments of the web page at various levels of the DOM tree. In the simple way I want to be able to obtain the boxes that enclose the display of the pages on the screen. e.g. consider this image: Image to visual of DOM structure as on display

This I drew using Chrome, which provides for each DOM node the corresponding rectangle on the display of the page in the browser. How can I get these boxes in my program so that I can process the page based upon how it looks on the screen.

我试图通过调用 SegmentPage() 函数（在 tesseractclass.h 中声明并在 pagesegmain.cpp 中定义）来获取图像块，但它只返回“文本块”......

我如何也可以获得图像块？

感谢您的回答。

伊莲

我有这张红外图像，它捕捉到了皮肤下的静脉。我想处理此图像，以便仅将静脉保留为黑色，并将其他所有内容都保留为白色。然后我想生成坐标像素作为这些黑色像素，它们将成为静脉。

这是我到目前为止所做的：

这是图像：

那么在此之后我该如何进行，以使静脉保持黑色，而其他任何东西都保持白色？

这是中值滤波后的图像，你可以看到静脉很暗。那么我该如何从这里开始，以使静脉保持黑色而其他一切保持白色呢？

我正在尝试在 MATLAB 中实现“基于 GrabCut 的对象分割技术”方法。为了最小化马尔可夫随机场能量函数，我使用这里提供的标准 Max-flow/min-cut 算法：http: //vision.csd.uwo.ca/code/

问题：当我尝试创建一个mex文件来调用MATLAB中的Graph创建和最小化函数时，我得到以下错误：

未定义符号：“Graph::Graph(int, int, void ( )(char ))”，引用自：GraphTest.o 中的_mexFunction

ld：未找到符号 collect2：ld 返回 1 个退出状态

mex：“GraphTest.mexmaci64”的链接失败。

我的代码真的很简单，只是复制了README文件中提到的部分代码如下：

如果以前使用过此软件包或遇到过类似错误的任何人都可以提供有关该问题的一些见解，我将不胜感激。

提前致谢！

我需要使用 PIL 将此图像切成三部分并选择中间部分。我该怎么做？

http://thedilbertstore.com/images/periodic_content/dilbert/dt110507dhct.jpg

我正在寻找基于线条和字符进行分割的示例。但我找不到任何有用的资源。如果有人可以为我提供一个如何做到这一点的例子，那就太好了..

我正在尝试为一个项目编写自己的分水岭分割实现。我有一个版本，它返回类似于给定非常微不足道的图片的正确分割的东西。不幸的是，它超级慢/效率低，它可能会或可能不会在所有情况下终止。

我一直在研究 Woods 和 Gonzales 的“数字图像处理”中的描述，以及 Watershed Wikipedia 页面。通用算法已编码并包含在下面，但我有一种感觉，我正在循环很多我不需要的东西。我在这里感谢任何和所有的帮助，在此先感谢。

我想分割网页并从背景中提取包含前景对象的框。请告诉我可以使用哪些方法。这是一个网页的示例图像以及我试图实现的相应输出，这是我手工制作的。

What is the best way to detect the corners of an invoice/receipt/sheet-of-paper in a photo? This is to be used for subsequent perspective correction, before OCR.

My current approach has been:

RGB > Gray > Canny Edge Detection with thresholding > Dilate(1) > Remove small objects(6) > clear boarder objects > pick larges blog based on Convex Area. > [corner detection - Not implemented]

I can't help but think there must be a more robust 'intelligent'/statistical approach to handle this type of segmentation. I don't have a lot of training examples, but I could probably get 100 images together.

Broader context:

I'm using matlab to prototype, and planning to implement the system in OpenCV and Tesserect-OCR. This is the first of a number of image processing problems I need to solve for this specific application. So I'm looking to roll my own solution and re-familiarize myself with image processing algorithms.

Here are some sample image that I'd like the algorithm to handle: If you'd like to take up the challenge the large images are at http://madteckhead.com/tmp

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In the best case this gives:

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However it fails easily on other cases:

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Thanks in advance for all the great ideas! I love SO!

EDIT: Hough Transform Progress

Q: What algorithm would cluster the hough lines to find corners? Following advice from answers I was able to use the Hough Transform, pick lines, and filter them. My current approach is rather crude. I've made the assumption the invoice will always be less than 15deg out of alignment with the image. I end up with reasonable results for lines if this is the case (see below). But am not entirely sure of a suitable algorithm to cluster the lines (or vote) to extrapolate for the corners. The Hough lines are not continuous. And in the noisy images, there can be parallel lines so some form or distance from line origin metrics are required. Any ideas?

_{(source: madteckhead.com)}