Robust image salient regional extraction and matching based on DoGSS-MSERs

The current paper presents a robust image salient region extraction and matching algorithm based on the maximally stable extremal regions in the difference of Gaussian scale space (DoGSS-MSERs) combined with scale-invariant feature transform (SIFT) algorithm and the maximally stable extremal regions...

Full description

Saved in:
Bibliographic Details
Published inOptik (Stuttgart) Vol. 125; no. 3; pp. 1469 - 1473
Main Authors Zhang, Meng-meng, Li, Ze-ming, Bai, Hui-hui, Sun, Yan
Format Journal Article
LanguageEnglish
Published Elsevier GmbH 01.02.2014
Subjects
Algorithms
DoGSS-MSERs
Extraction
Feature extraction
Gaussian
Matching
Mathematical analysis
MSERs
Region matching
SIFT
Stable salient region
Transforms
DoGSS-MSERs
SIFT
MSERs
Region matching
Stable salient region
Online AccessGet full text

Cover

More Information
Summary:The current paper presents a robust image salient region extraction and matching algorithm based on the maximally stable extremal regions in the difference of Gaussian scale space (DoGSS-MSERs) combined with scale-invariant feature transform (SIFT) algorithm and the maximally stable extremal regions (MSERs) algorithm. First, the difference of Gaussian scale space (DoGSS) is constructed using image scale-space theory. The maximally stable extremal regions are then calculated and the stable component is extracted with blur-invariant and scale-invariant property using the stable method in the DoGSS. Finally, the regions are described with a novel region descriptor, thereby achieving matching. The experiments show that the feature regions extracted in the current paper inherit the good properties of SIFT and MSERs (scale-invariant and affine-invariant) and are more stable and more accurate for matching.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0030-4026
1618-1336
DOI:10.1016/j.ijleo.2013.09.007