Embedded double matching of local descriptors for a fast automatic recognition of real-world objects

Author

Alqaisi, T. ; Gledhill, D. ; Olszewska, J.I.

Author_Institution

Sch. of Comput. & Eng., Univ. of Huddersfield, Huddersfield, UK

fYear

2012

fDate

Sept. 30 2012-Oct. 3 2012

Firstpage

2385

Lastpage

2388

Abstract

In this paper, we present a new approach for matching local descriptors such as Scale Invariant Feature Transform (SIFT) ones in order to recognize image objects quickly and reliably. The proposed method first computes the Hausdorff distance combined with the City-Block distance to match the two sets of the extracted keypoints from the goal and data images, respectively. Then, the matched points are involved into an embedded pairing process, leading to a double matching which is more discriminant for the object recognition purpose as demonstrated on real-world standard databases.

Keywords

embedded systems; feature extraction; image matching; object recognition; visual databases; City-Block distance; Hausdorff distance; data images; embedded double matching; embedded pairing process; fast automatic recognition; image object recognition; keypoint extraction; local descriptor matching; real-world objects; real-world standard databases; Computer vision; Conferences; Databases; Euclidean distance; Feature extraction; Object recognition; Robustness; City-Block Distance; Euclidean Distance; Feature Matching; Hausdorff Distance; Interest Points; Object Recognition; SIFT; Similarity Measure;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2012 19th IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1522-4880

Print_ISBN

978-1-4673-2534-9

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2012.6467377

Filename

6467377