DeepAI
Log In Sign Up

Foreground segmentation based on multi-resolution and matting

02/10/2014
by   Xintong Yu, et al.
0

We propose a foreground segmentation algorithm that does foreground extraction under different scales and refines the result by matting. First, the input image is filtered and resampled to 5 different resolutions. Then each of them is segmented by adaptive figure-ground classification and the best segmentation is automatically selected by an evaluation score that maximizes the difference between foreground and background. This segmentation is upsampled to the original size, and a corresponding trimap is built. Closed-form matting is employed to label the boundary region, and the result is refined by a final figure-ground classification. Experiments show the success of our method in treating challenging images with cluttered background and adapting to loose initial bounding-box.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/11/2015

LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes

One popular approach to interactively segment the foreground object of i...
06/29/2015

An automatic and efficient foreground object extraction scheme

This paper presents a method to differentiate the foreground objects fro...
12/16/2014

A Robust Regression Approach for Background/Foreground Segmentation

Background/foreground segmentation has a lot of applications in image an...
12/12/2012

Adaptive Foreground and Shadow Detection inImage Sequences

This paper presents a novel method of foreground segmentation that disti...
08/24/2018

Automatic Foreground Extraction using Multi-Agent Consensus Equilibrium

While foreground extraction is fundamental to virtual reality systems an...
01/21/2004

Better Foreground Segmentation Through Graph Cuts

For many tracking and surveillance applications, background subtraction ...
05/21/2020

Unsupervised segmentation via semantic-apparent feature fusion

Foreground segmentation is an essential task in the field of image under...