The level set function is a way to define a known outline of an object by setting the pixels inside the outline to positive values, the pixels on the outside to negative values and the pixels around the outline to 0.Thus the zero level of the level set function is where the outline is found.

An active contour model (using level sets) is the optimisation technique used to find an unknown outline based on an initial contour and iterative procedures to optimise the zero level until the outline is found.

Thank you very much Veronica Biga

Active contour is a boundary based segmentation: Snake id perhaps the classical model

Level set is also a contour model based on curve evoultion

http://www2.maths.lth.se/vision/bildanalys/nu/f07

Active contours have been widely used for image segmentation and boundary tracking since the first introduction of snakes by Kass et al.It's an attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries.

The basic idea is to start with initial boundary shapes represented in a form of closed curves, i.e. contours, and iteratively modify them by applying shrink/expansion operations according to the constraints of the image. Those shrink/expansion operations, called contour evolution, are performed by the minimization of an energy function like traditional region-based segmentation methods or by the simulation of a geometric partial differential equation (PDE).

There are two main approaches in active contours based on the mathematic implementation: snakes and level sets. Snakes explicitly move predefined snake points based on an energy minimization scheme, while level set approaches move contours implicitly as a particular level of a function. Level set theory, a formulation to implement active contours, was proposed by Osher and Sethian. The use of level set theory has provided more flexibility and convenience in the implementation of active contours.

Hi dear researcher....

"Segmentation" is an intermediate process in image processing that plays a key role in image analysis and machine vision.

Various methods have been proposed for image segmentation. One of the most common segmentation methods is " deformable model".

The goal of "deformable model" is to "minimize internal energy functions" (causing the curve to smooth) and "external energy functions" (causing the curve to lead to the object boundary).

deformable model can be divided into two groups:

1. deformable model of parameter (explicit) which is also called "Active Contour".

2. deformable model of Non-parameter (geometric)( Implicit), also called "Level Set" or " Geometric active contour ".

The active contour (snake) is a curve bounded by continuity and smoothness and at the same time drawn by an external force field towards the border points and edges of the image.

The level set method is based on the theory of curve evolution. The most important level set methods include the following:

1- Caselles method

2- Chan & Vese method

3- Chunming Li method

4- Lankton method

5- Bernard method

6- Shi method

...

I hope it is useful

There are two main approaches in active contours based on the mathematic implementation: snakes and level sets. Snakes explicitly move predefined snake points based on an energy minimization scheme, while level set approaches move contours implicitly as a particular level of a function