Literature Review of Energy Minimization Based Image Segmentation Methods

The process of image segmentation in computer vision is nothing but extracting or grouping of image objects into various parts. Such extracted parts from image are having information that humans can easily view and separate all objects individually. There is no direct way to recognition such objects using computers, hence we need to have method which can efficiently extract objects from input image for further processing. There are many methods presented for image segmentation in image processing domain. Such segmentation methods are based on different image features. In this paper we are focusing on field of energy minimization as it applies to image segmentation. The traditional methods for image segmentation are unreliable, noise insertion, shape variation. Therefore to address this limitations and achieves better image segmentation since from last decade energy minimization based methods introduced by various researchers due to their characteristic of considering various segmentation goals. In general, process of energy minimization methods is based on evaluating the segmentation goodness for input image, and then further minimization of evaluated function resulted into image segmentation. In this paper we are presented the survey on different energy minimization based image segmentation methods with their advantages and disadvantages. Basically all this methods falls under the category of level set function based methods.