The Application Level Set Method Basedon Boundary Constraint

The Level Set method is proposed by American mathematicians Stanley Osher and James Sethian, the basic principle of this method is the curve evolution, it is embedded in a higher dimensional space as zero horizontal level set function. The advantage of the Level Set method is that one can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects. Also, the Level Set method makes it very easy to follow shapes that change topology. And the calculation method can be used in higher dimension. Because of its good properties, it has become popular in many disciplines, such as image processing, computer graphics, computational geometry, optimization, and computational fluid dynamics.Li proposed a model: level set evolution without re-initialization a new variational formulation. The level set function can be initiatized with general functions that are more efficient to construct and easier to use in practice than the widely used signed distance function. The level set evolution in our formulation can be easily implemented by simple finite difference scheme and is computationally more efficient. However almost all of the level set method can deal with those objects color which is obvious difference with the background , otherwise it will produce over-segmentation. In this paper, a new model based on boundary constraint level set is proposed. This method utilizes the property that the object boundary has approximately equal value. This restrictions will make the segment step more quickly and accurately.Estimating color-temperature about automatic white balance algorithm is not accurate. This paper proposed a method estimating color temperature based on the level set. First of all, change RGB color images to YCbCr color space, then calculate the sign distance function, through the difference about brightness, chromatism and the threshold, the evolution of the level set will remove those isolated pixels and the normal approximation white pixels, make more accurately to estimate color temperature.