Template Matching Advances and Applications in Image Analysis

Authors

  • Nazanin Sadat Hashemi Islamic Azad University, North Tehran Branch, Faculty of Engineering, Department of Information Technology, 79 Ghobadian St., Africa St., Tehran, Iran, 1969633651
  • Roya Babaei Aghdam Islamic Azad University, North Tehran Branch, Faculty of Engineering, Department of Information Technology, 79 Ghobadian St., Africa St., Tehran, Iran, 1969633651
  • Atieh Sadat Bayat Ghiasi Islamic Azad University, North Tehran Branch, Faculty of Engineering, Department of Information Technology, 79 Ghobadian St., Africa St., Tehran, Iran, 1969633651
  • Parastoo Fatemi Islamic Azad University, North Tehran Branch, Faculty of Engineering, Department of Information Technology, 79 Ghobadian St., Africa St., Tehran, Iran, 1969633651

Keywords:

Template Matching, Image Processing, Image Analysis.

Abstract

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition.

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Published

2016-11-20

How to Cite

Hashemi, N. S., Aghdam, R. B., Ghiasi, A. S. B., & Fatemi, P. (2016). Template Matching Advances and Applications in Image Analysis. American Scientific Research Journal for Engineering, Technology, and Sciences, 26(3), 91–108. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/2378

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