The Video Object Extraction Method Based on the Intersection of Three Times Frame Change Edge

A Geographical Object Extraction Method Using User's Trajectories for Generating Artificial Maps

2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) ◽

10.1109/imcom51814.2021.9377367 ◽

2021 ◽

Author(s):

Koki Ohtsuka ◽

Daisuke Kitayama ◽

Kazutoshi Sumiya ◽

Kazushi Fujita ◽

Goto Shin ◽

...

Keyword(s):

Extraction Method ◽

Object Extraction

Download Full-text

An Automatic Object Extraction Method Using Color Features Of Object And Background In Image

Journal of Digital Convergence ◽

10.14400/jdpm.2013.11.12.459 ◽

2013 ◽

Vol 11 (12) ◽

pp. 459-465 ◽

Cited By ~ 2

Author(s):

Sung Kap Lee ◽

Young Soo Park ◽

Gang Seong Lee ◽

Jong Yong Lee ◽

Sang Hun Lee

Keyword(s):

Extraction Method ◽

Object Extraction ◽

Color Features

Download Full-text

Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.17610 ◽

2018 ◽

Vol 7 (4) ◽

pp. 2598

Author(s):

G S. Gowri ◽

Dr. P. Ponmuthuramalingam

Keyword(s):

Random Field ◽

Dirichlet Process ◽

Video Segmentation ◽

Target Object ◽

Object Extraction ◽

Contour Tracking ◽

Video Object ◽

Object Boundary ◽

Current Frame ◽

Film Making

Video object extraction (VOE) using segmentation from a video sequence is a very important task in editing and multimedia analysis for film making. Most of the VOE approaches required prior knowledge about background and foreground to extract target objects. In this paper, an Optimized smoothed Dirichlet Process Multi-view learning with improved adaptive Modified Markov Random Field which is enhanced by adaptive shape prior modified graph cut (OsDPMVL-IASMMRF) model has been extended for video-based object extraction. The contour tracking has been additionally included OsDPMVL-IASMMRF for VOE. The Teh–Chin algorithm has been used with OsDPMVL-IASMMRF for predicting the contour in the current frame by matching the extracted object contour from the previous segmented frame. The contour tracking propagates the shape of the target object, whereas the OsDPMVL-IASMMRF segmentation refined the object boundary and the shape for enhancing the accuracy of video segmentation. The experimental outcomes show that the proposed approach provides better segmentation results in terms of accuracy, precision and recall.

Download Full-text