A Novel Video Object Segmentation Approach for Noisy Video Sequences towards Effective Video Retrieval

Abstract

Abstract-In modern times, video object segmentation has emerged as one of the most imperative and challenging area of research. The principal objective of video object segmentation is to facilitate content-based representation by extracting objects of interest from a series of consecutive video frames. Recently, a number of video object segmentation algorithms have been discussed and unfortunately most existing segmentation algorithms are not adequate and robust enough to process noisy video sequences. Competence of most segmentation techniques is affected by the presence of noise in frames which is a critical issue of edge preservation. This paper presents a novel video object segmentation approach for noisy color video sequences towards effective video retrieval. Initially, the noisy video frames are denoised using a strategy based on an enhanced sparse representation in transform domain. Afterwards, the background is estimated from the denoised frames using the Expectation Maximization (EM) algorithm. Then, the foreground objects i.e.) moving video objects are segmented with the aid of the novel approach presented. The biorthogonal wavelet transform and the L2 norm distance measure are employed in the foreground object segmentation. The experimental results demonstrate the effectiveness of the presented approach in segmenting the video objects from noisy color video sequences.

Motion Estimation of Videos-Objects by Finite Elements Mesh-Size

“…• These coordinate are used as functions to calculate the point surface of interpolation to deform; by using the functions of interpolation; calculate the solution corresponding to the type movements by the finite elements method and the learning algorithm [15].…”