English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this article, we put forward a novel region matching based motion estimation scheme to detect objects with accurate boundaries from video sequences. We have proposed a fuzzy edge incorporated Markov Random Field (MRF) model based spatial segmentation scheme that is able even to identify the blurred boundaries of objects in a scene. Expectation Maximization (EM) algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough knowledge of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. Moving objects in the scene are detected by the proposed χ2-test based local histogram matching. It is noticed that the proposed scheme provides better results with less object background misclassification as compared to optical flow and label fusion based techniques.

Moving Objects Detection from Video Sequences Using Fuzzy Edge Incorporated Markov Random Field Modeling and Local Histogram Matching