A New Approach for Noise Removal and Video Object Segmentation Using Color Based Fuzzy C-Means Technique

Video transmission plays a very important role in traffic applications. Noise can be a big offence in affect-ing encoding efficiency because it can be present throughout an entire application. Noise has the technical definition for various anomalies and unnecessary variations that get built-in into a video signal. Noise re-duction enables better video quality at lower bit rates by making the source look better and decrease the video complication prior to the any process. In this proposed method we adapted the spatial video denois-ing methods, where image noise are reduced and are is applied to each frame individually. Since there is a great deal of removing noise from video content, this paper has been devoted to noise detection and filter-ing methods that aims the removing unwanted noise without affecting the clarity of scenes which contains necessary information and rapid movement. The aim of this work is to produce the exact intensity information of segmentation’s neighborhood relationships [1]. In this paper, foreground based segmentation; fuzzy c-means clustering segmentation is compared with the proposed method fuzzy c – means segmentation based on color. This was applied in the video frame to segment various objects in the current frame. The proposed technique is a commanding method for image segmentation and it works for both single and multiple featured data for spatial information. Strong techniques were introduced for finding the number of components in an image. The results done experimentally shows that the proposed segmentation approach generates good quality segmented frames. This paper deals with efficient analysis of noise removal techniques and enhancing the segmentation in video frames.