| Abstract [eng] |
Image segmentation is used in many fields of science such as geography, geology and biology, so that we can distinguish and analyze objects in pictures. When selecting segmentation algorithm we usually draw attention to its performance, complexity and adaptation to different types of images. As we usually are processing complex structure and lower-quality images, it is very important that the segmentation algorithm would be suitable for all classes of images. Mumford Shah functional is one of the most popular and effective tools for image segmentation. The algorithm is characterized by easy and fast realization. The biggest advantage of this algorithm is the ability to adapt to different types of images. By changing the Mumford Shah functional formula‘s incoming parameter‘s values, we can affect the segmentation results. In this work we are selecting the most suitable parameter‘s values for different types of images in order to obtain the best segmentation results. Obtained results are based on statistical analysis. |
