Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 145
  • Download : 0
Most features of image-based coin recognition have been based on histogram information to achieve rotation-invariant property. However, discrimination of the features based on histogram information can be reduced by ignoring local spatial structure. In this paper, we propose a novel feature of image-based coin recognition that exploits a spatial structure. In order to consider the structure of a coin, rotation-and-flipping-robust region binary patterns (RFR) is adopted. The proposed method computes gradient magnitudes in a coin image, and extracts RFR using local difference magnitude transform to increase the accuracy of coin recognition. Comparative experiments with a number of state-of-the-art methods have been performed on the MUSCLE CIS-Benchmark Preview data set. The experimental results showed that the proposed method outperformed the state of the art methods in terms of recognition accuracy, smaller feature dimension, and shorter feature extraction time.
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Issue Date
2015-10
Language
English
Article Type
Article
Keywords

MULTIRESOLUTION GRAY-SCALE; TEXTURE CLASSIFICATION

Citation

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.32, pp.217 - 223

ISSN
1047-3203
DOI
10.1016/j.jvcir.2015.08.011
URI
http://hdl.handle.net/10203/205268
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0