Keywords
SIFT descriptor
Geometric Hashing algorithm
How to Cite
Abstract
In this paper we develop an algorithm for logo detection and grouping in images. For logo detection, the “Scale-Invariant Feature Transform” (SIFT) descriptor is used, which is one of the most studied and used in pattern recognition in the fields of image analysis and computer vision. We have developed a geometric algorithm for grouping and counting the detected logos. This algorithm is based on the so-called “Geometric Hashing” algorithm. Finally, we perform some tests in order to analyze the robustness of the algorithm.
References
Hu, M. 1962. "Visual Pattern Recognition by Moment Invariants". IRE Trans. Information Theory. 2 (IT-8), 179-187.
Pratt, W. 2007. "Digital Image Processing", Willey, 4th edition.
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Van Gool, L. 2005. "A Comparison of Affine Region". International Journal of Computer Vision. 65 (1-2), 43-72.
Lowe, D. 2004. "Distinctive Image Features from Scale - invariant Keypoints". International Journal on Computer Vision. 60 (2), 91-110.
Forsyth, D. and Ponce, J. 2002. "Computer Vision: A Modern Approach", Prentice Hall.
Mikolajczyk, K. and Schmid, C. 2003. "A Performance Evaluation of Local Descriptors". Proceedings of IEEE Conference on Computer Vision and Pattern.
Copyright notice
Authors who publish in the journal ACI Avances en Ciencias e Ingenierías accept the following terms:
- The authors will retain their copyright and guarantee the journal the right of first publication of their work, which will be simultaneously subject to the Creative Commons Attribution License that allows third parties to share the work provided that its author and its first publication in this journal is indicated.
- Authors may adopt other non-exclusive license agreements for the distribution of the published version of the work, thereby being able to publish it in a monographic volume or reproduce it in other ways, provided that the initial publication in this journal is indicated.
- Authors are permitted and advised to disseminate their work over the Internet:
- Before submission to the journal, authors can deposit the manuscript in pre-publication files/repositories (preprint servers/repositories), including arXiv, bioRxiv, figshare, PeerJ Preprints, SSRN, and others, which can produce interesting exchanges and increase citations of the published work (see The effect of open access).
- After submission, it is recommended that authors deposit their article in their institutional repository, personal website, or scientific social network (such as Zenodo, ResearchGate or edu).