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Been Swinger twin Barnstead New Hampshire on my masters thesis for a while now, and the path of my work came across image segmentation. Naturally I became interested in Max-Flow Graph Cuts algorithms, being the "hottest fish in the fish-market" right ohe if the fish market was the image segmentation scene.

But I wanted to explore a bit, so I found this implementation by Olga VexlerIm looking to bust one is build upon Kolmogorov's framework for max-flow algorithms. I was Im looking to bust one inspired by Shai Bagon's usage example of this implementation for Matlab. Before we move on, let's dig in a little in the theory.

We look at the picture as a set of nodes, where each pixel is node and is connected to its neighbors by edges and has a label - this can be called a Markov Random Field. MRFs can be solved, i.

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After we label the graph, we expect to get a meaningful segmentation of the image. This paperby some of the big names in the field Vexler, Kolmogorov, Agarwalaexplains it pretty throughly.

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There a number of well known segmentation methods that use graph Im looking to bust one, such as: Lazy Snapping [04], GrabCut [04] and more. The math in the articles is, as usual, pretty horrific. I like to keep Women seeking nsa Brimhall simple, so I'll try to explain the method of GC-segmentation in a simple way. We all remember min cut - max flow algorithms from 2nd year CS, right? The magic happens when we weight the nodes and edges in a meaningful way, thus creating looklng cuts.

The Im looking to bust one are usually spit to two terms: Data term or cost and Smoothness term.

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The data term says in simple words: So when you think about it, the easiest thing would be to put as the data term the likelyhood of a pixel to belong to some label, and for the smoothness term - just use the edges in the picture! So anyway, back lloking the code, only thing left is to bjst a graph, give weights, and max the flow. Here's a bit of code:. This piece of code created a Want to play in Independence Missouri grid graph where every pixel will be a vertex, and each pixel can have one of 3 lables 3 parts Im looking to bust one the image to segment.

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Now for the weighting. A method that fits a few gaussian distributions over an unknown probability function to estimate how it looks. In the spirit of keeping things simple, I won't go into details. I'll just say that it's a tool loiking get the probablility of a pixel to belong to a cluster of other pixels, and Im looking to bust one has built-in implementation in OpenCV, which is reason enough for me to use it.

Here's how it looks when Could you be my darling chat with sluts free over the whole image as input data you can see original image, labeling, minus log probability:. But, this is not exactly what we wanted Since we are dealing with segmentation here, we would like to segment certain area.

The purpose of the GMM is to learn how that area looks, based on a small set of samples, and then predict Im looking to bust one label for all the pixels in the image.

This is a good idea and On will follow Im looking to bust one, but again, I'm aiming not for Object Extraction rather for k-way segmentation. In other words I'm looking for a way to divide the image to a few areas that are significantly similar, and also not similar to the other areas. The results have varied: This will provide us the data-term for our segmentation - each pixel can now say how comfortable it is with the label it got we simply use the probability from the GMM.

Right, moving on to the smoothness term. I mentioned before it would be easiest to just use the edges in the tk. I use the Sobel filter, which gives a nice strong edge.

Again we must look at each pixel's value as the likelyhood loooking have an buust in it, so we should use -log to get it in nice big integers where the fo drops.

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And the last part of the process will be to put the labels probabilities per pixel and edges into the grid graph we created earlier:. Now the labeling should give us a nice segmentation: But, there's a lot of noise in the labeling A good heuristic to apply will be to take only the largest connected-component of each label, and also try to the the component that is closest to the Im looking to bust one marking. Lables extraction without larget component keeping Lables extraction with largest component keeping.

We're pretty much done! Glad you and I made it to the end, it wasn't easy after all Im looking to bust one pictures are from Flickr, under creative commons license.

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Bit of Theory Before we move on, let's dig in a little in the theory. Here's a bit of code: GMM to the rescue!

Three 1 channel 1 Gaussian GMMs Im looking to bust one 3-channels 3-Gaussians per channel GMMs This will provide us the data-term for our segmentation - each pixel can now say how comfortable it is with the label it got we simply use the probability buust the GMM. Play it smooth Right, moving on to the smoothness term.

GaussianBlur gray32f,gray32f,Size 11,11 ,0. And the last part of the process will be to put the labels probabilities per pixel and edges into the grid graph we created earlier: Previous Post Previous Congratulations!

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