Cv - Levelset Method Research And Its Application In Weather Target Segmentation

There are lots of targets in the meteorological image, the overall is discrete and not continuous, but a single target is continuous. It is difficult to accurately split the specific targets of interest, so meteorological image object segmentation is different from the general image segmentation. Image segmentation methods,such as the snake model, the gradient vector flow model, use dynamic optimization of energy function to approximate the true target outline, to expand external range and to enhance the attractiveness of the target depression contour edges. When these methods were improved, many new problems were brought forth such as the initial contour outline was sensitive, parameters set was complicated,contours were redundant, computation time were long,etc.The main work based on the study of CV model and level set method is as follows:Firstly, this paper studied and analyzed the level set method and its theory of curve evolution and the Mumford-Shah model, as well as described their basic idea, then deduced the CV model, and analyzed its numeral solution and its summary of advantages and disadvantages.The CV model utilized the global gray information of the image, can not effectively segment the uneven gray-scale or complex background of the target images; the CV model combined level set method needs extensive numerical calculation, leading to can not completely segment within the limited time. To solve the first problem, the image gradient was added into the model that CV model combined level set method to resolve the problem caused by the uneven gray-scale, To the second problem, on the basis of the solved the first problem, we present two solutions, one is to change the zero value of the energy function, the other is to use to improve the method of calculation format instead of the original computational scheme to numerically solve, while for the initial the radius of the level set function is determined by the maximum, which in turn accelerates the evolution of the curve outline. Finally, our model combines the characteristics of meteorological images, and the set for the meteorological parameters of the image were discussed, segmentation system was designed and developed. Experimental analysis proved that the improved model is to better handle multi-objective images, fast calculation, accurate segmentation, redundancy evolution curve,and it meets the requirements of real-time meteorological images.