Open Access

ARTICLE

An Optimized Scale-Invariant Feature Transform Using Chamfer Distance in Image Matching

Tamara A. Al-Shurbaji¹, Khalid A. AlKaabneh², Issam Alhadid^3,*, Ra’ed Masa’deh⁴

1 College of Computer Science and Informatics, Amman Arab University, Amman, Jordan
2 Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan
3 Faculty of Information Technology and Systems, University of Jordan, Aqaba, Jordan
4 School of Business, University of Jordan, Amman, Jordan

* Corresponding Author: Issam Alhadid. Email:

(This article belongs to the Special Issue: Recent Trends in Computational Methods for Differential Equations)

Intelligent Automation & Soft Computing 2022, 31(2), 971-985. https://doi.org/10.32604/iasc.2022.019654

Received 21 April 2021; Accepted 20 June 2021; Issue published 22 September 2021

Abstract

Scale-Invariant Feature Transform is an image matching algorithm used to match objects of two images by extracting the feature points of target objects in each image. Scale-Invariant Feature Transform suffers from long processing time due to embedded calculations which reduces the overall speed of the technique. This research aims to enhance SIFT processing time by imbedding Chamfer Distance Algorithm to find the distance between image descriptors instead of using Euclidian Distance Algorithm used in SIFT. Chamfer Distance Algorithm requires less computational time than Euclidian Distance Algorithm because it selects the shortest path between any two points when the distance is computed. To validate and evaluate the enhanced algorithm, A data set with (412) images including: (100) images with different degrees of rotation, (100) images with different intensity levels, (112) images with different measurement levels and (100) distorted images to different degrees were used; these images were applied according to four different criteria. The simulation results showed that the enhanced SIFT outperforms the ORB and the original Scale-Invariant Feature Transform in term of the processing time, and it reduces the overall processing time of the classical SIFT by (41%).

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Keywords

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