Publication Type

Conference Proceeding Article

Publication Date

12-2010

Abstract

Extracting textured objects from natural scenes is a challenging task in computer vision. The main difficulties arise from the intrinsic randomness of natural textures and the high-semblance between the objects and the background. In this paper, we approach the extraction problem with a seeded region-growing framework that purely exploits the statistical properties of intensity inhomogeneity. The pixels in the interior of potential textured regions are first found as texture seeds in an unsupervised manner. The labels of the texture seeds are then propagated through their respective inhomogeneous neighborhoods, to eventually cover the different texture regions in the image. Extensive experiments on a large variety of natural images confirm that our framework is able to extract accurately the salient regions occupied by textured objects, without any complicated cue integration and specific priors about objects of interest.

Keywords

Cue integration, Intensity inhomogeneity, Intrinsic randomness, Natural images, Natural scenes, Natural textures, Salient regions, Seeded region, Statistical properties, Textured objects, Textured regions

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Management and Analytics

Publication

Computer vision: ACCV 2009: 9th Asian Conference on Computer Vision, Xi'an, September 23-27

Volume

5996

First Page

1

Last Page

10

ISBN

9783642122965

Identifier

10.1007/978-3-642-12297-2_1

Publisher

Springer Verlag

City or Country

Cham

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1007/978-3-642-12297-2_1

Share

COinS