Abstrak

Kembali

Digital image processing allows one to enhance image features of interest while attenuating details irreverent to a given application, and then extract useful information about the scene from the enhanced image. It comprises of two techniques i.e. Image compression and Image Segmentation. Image compression is used to minimize the amount of memory needed to represent an image. Images often require a large number of bits to represent them, and if the image needs to be transmitted or stored, it is impractical to do so without somehow reducing the number of bits. Image segmentation is the process of partitioning a digital image into multiple segments or in sets of pixels. Image segmentation is typically used to locate objects and boundaries like lines, curves, etc. in images. Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. This paper proposed a GMM based MRF model using k-mean clustering because GMM is hierarchical clustering, shows the clustering details and smoothness and continuity of color regions are adopted enforced with the adaptation of MRF. This technique takes more computational time and quite expensive in memory.