Combined Approach for Image Segmentation

TL;DR

This paper presents a combined image segmentation method that enhances contrast, reduces noise, and applies fuzzy c-mean clustering to produce better segmented images efficiently.

Contribution

It introduces a novel combined approach integrating histogram equalization, median filtering, and fuzzy c-mean clustering for improved image segmentation.

Findings

Enhanced segmentation quality with less computation time

Effective noise removal and contrast enhancement

Improved segmentation accuracy over traditional methods

Abstract

Many image segmentation techniques have been developed over the past two decades for segmenting the images, which help for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing. In this, there is a combined approach for segmenting the image. By using histogram equalization to the input image, from which it gives contrast enhancement output image .After that by applying median filtering,which will remove noise from contrast output image . At last I applied fuzzy c-mean clustering algorithm to denoising output image, which give segmented output image. In this way it produce better segmented image with less computation time.