Description

Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Image Features are the basis for most of the real time image processing applications. Edge is one of the prime features of image. It helps us to analyze, infer and take decision in various image processing applications. In this project the system uses Edge feature based XSG image segmentation process. This scheme is very effective. Due to the advent of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. With the improvement of computer processing capabilities and the increased application of color image, the color image segmentation are more and more concerned by the researchers. This system proposes a supervised variation level set segmentation model in this project. In addition kNN (K-Nearest neighbor) is a technique performed with supervision with classes formulated before classification after deciding the classification which is unaffected by ‘highly correlated feature’ issue providing results with accuracy. The results indicate that the degree of accuracy of classification fMRI dataset using kNN classifier is more than that of GMM classifier.