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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages2385-2388
Number of pages4
DOIs
Publication statusPublished - 2012
Event19th IEEE International Conference on Image Processing - Lake Buena Vista, United States
Duration: 30 Sep 20123 Oct 2012
Conference number: 19

Conference

Conference19th IEEE International Conference on Image Processing
Abbreviated titleICIP 2012
CountryUnited States
CityLake Buena Vista
Period30/09/123/10/12

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Alqaisi, T., Gledhill, D., & Olszewska, J. I. (2012). Embedded double matching of local descriptors for a fast automatic recognition of real-world objects. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings (pp. 2385-2388). [6467377] https://doi.org/10.1109/ICIP.2012.6467377