Computer Vision 2

There are other techniques to image segmentation. Some of them are enumerated below:
Region growing methods: Here seeds mark each of the objects to be segmented. The regions are iteratively grown by comparing all unallocated neighbouring pixels to the region. The difference between a pixel's intensity value and the region's mean is used as a similarity measure.
Split and merge methods: This method splits the image to four quadrants and then they can be merged if they are found to be homogeneous.
Partial Differential equation based methods: PDE methods involve setting up an equation such as a curve propagation or Lagrangian equation that parameterizes the contours. To choose the contour also called snake derivatives are computed using finite differences  and derivatives. Between similar choices, the steepest gradient descent is chosen for minimizing the energy. Thus this leads to fast and efficient processing.
Level Set Methods: The level set method was proposed to track moving interfaces. It can be used to efficiently address the problem of curve/surface propagation in an implicit manner. The central idea is to represent the evolving contour using a signed function where its zero level corresponds to the actual contour. Then according to the motion equation of the contour, one can easily derive a similar flow for the implicit surface that when applied to the zero level will reflect the propagation of the contour.
Fast marching methods specify the evolution of a closed curve as a function of time T and speed F(x) in the normal direction at a point x  on the curve. The speed function is specified, and the time at which the contour crosses a point x is obtained by solving the equation.
Graph Partitioning methods can effectively be used for image segmentation. In these methods,, the image is modeled as a weighted undirected graph.