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Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 569–587 | Cite as

An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications

  • Ali K. Hmood
  • Ching Y. Suen
  • Louisa Lam
Proceedings of the 6th International Workshop
  • 1 Downloads

Abstract

The Histogram of Oriented Gradients (HOG) is one of the most widely used methods to extract the gradient features for object recognition and consistently shows high accuracy rates when compared to other descriptors. The major drawbacks of using the HOG method are the necessity of finding an optimal window size to fit the whole object; and the exhaustive search mechanism represented by a fixed window size sliding through the whole image to locate and recognize objects. This research proposes an efficient and robust Dynamic-HOG as an improvement to the traditional HOG method to locate and recognize structured objects in images. The proposed method works by locating and analyzing the structured objects in images in order to define a dynamic window size w.r.t. each object size. Moreover, the Dynamic-HOG method requires much less processing time by eliminating the exhaustive search mechanism. The method defines the height and width thresholds of objects and bounds each object with a window w.r.t. its size while ignoring non–object edges. It fits structured objects of a close range of heights and widths. This paper considers the characters that are minted on coins of different languages and sizes as the objects to recognize. There are several papers in the literature discussing coin recognition problem and proposing solutions based on various sets of features extracted from the entire coin image. This research also proposes a new method for coin recognition by focusing on recognize coins based on smaller part of the coin image which are the characters. Our method is evaluated on coins from diverse countries with different background complexity. The proposed method achieved precision and recall rates as high as 98.08 and 98.23%, respectively; which demonstrate the effectiveness and robustness of the proposed method.

Keywords

computer vision dynamic histogram of oriented gradients character recognition coin recognition 

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Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Centre for Pattern Recognition and Machine Intelligence (CENPARMI) Department of Computer Science and Software Engineering Concordia UniversityMontrealCanada

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