IMPACT OF IMAGE SEGMENTATION APPROACHES ON NOISY AND BLURRED IMAGES

In today’s world, many applications are used for image processing. Segmentation is one of the main steps used for image processing. Segmentation is used to identify objects in an image. It divides an image into multiple segmentations. There are hundreds of techniques present that are used to segment an image. Clustering is one of the techniques. K nearest neighbor and K - mean techniques are two clustering techniques of segmentation. The main principle of clustering technique is to make cluster of pixels on the basis of distance between pixels and centroids. This paper gives details about KNN and K - mean techniques and th eir efficiency. In past, these images are applied a lot on smooth images. In this paper, the efforts have been done to analyze the impact of these techniques on various non - efficient images. The functions used to make the images degraded are noise and blur in the image. It has been observed that depending on various parameters both the algorithms performs in similar manner except some differences which are highlighted in this paper.