In GrabCut they pretty much create a GMM for every logical "cluster" they want to segment: Positively Background, Probably Background, Probably Foreground and Positively Foreground. This is a good idea and I will follow it, 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 math in the articles is, as usual, pretty horrific. I like to keep things 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? well segmentation using GC is not very different. The magic happens when we weight the nodes and edges in a meaningful way, thus creating meaningful cuts. The weights are usually spit to two terms: Data term (or cost) and Smoothness term. The data term says in simple words: "How happy this pixels is with that label", and the smoothness term pretty much says "How easy can a label expand from this pixel to that neighbor". 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!
Thesis on color image segmentation - goffinet …
Digital Image Processing Projects are focused two dimensional and three dimensional images for processing. Different types of images are used for implementing the concepts. There are intensity transformations and spatial filtering, frequency based filtering, image restoration and reconstruction, wavelet and multi-resolution processing, color image processing, morphological image processing, image compression, image representation, segmentation and object recognition.
RGBD image segmentation code for matlab - Stack Overflow
a segmentation method is proposed to handle degraded Research on Random Walks Image Segmentation MethodAccording to certain similar criteria of some low-level visual features, such as color, texture, shape etc, image segmentation refers to partition a single image into Developers of the ISMS Software - For NISPOM Compliance of classified information including document control, thesis.pdf | Image Segmentation MRI Brain image segmentation using graph cutsÂ PDF file1 MRI Brain image segmentation using graph cuts Thesis for the degree of Master of Science Mohammad Shajib Khadem Supervisor …Medical image segmentation in volumetric CT and MR …This portfolio thesis addresses several topics in the field of 3D medical image analysis.
"Color Separation for Image Segmentation" by Meng Tang
Section 2: Image Segmentation Methods 6 2.Image Segmentation and Multiple skew estimation Â PDF fileImage Segmentation and Multiple skew estimation, correction in printed and handwritten documents Thesis submitted in partial ful llment of the requirements1.
Color Image Segmentation Based on Bayesian Theorem …
SEEDS Revised is a new implementation of the superpixel algorithm SEEDS  used for evaluation in my bachelor thesis “Superpixel Segmentation using Depth Information”. This article introduces the basic concepts of SEEDS as well as the usage of SEEDS Revised.
The Gray Segmentation of Color Image Based on ..
...ave a more flexible and rich approach, we propose to use a multiscale segmentation, that is, the partitioning is composed of a hierarchical pyramid with successive levels more and more simplified. In =-=-=- we have introduced two algorithms for hierarchical color image segmentation. The first one is based on a non-parametric pyramid of watersheds, comparing different color gradients. The second segmenta...