IIUM Repository

A Comparative Analysis of Feature Extraction Algorithms for Augmented Reality Applications

Alam, Mir Suhail and Morshidi, Malik Arman and Gunawan, Teddy Surya and Olanrewaju, Rashidah Funke (2021) A Comparative Analysis of Feature Extraction Algorithms for Augmented Reality Applications. In: 2021 IEEE 7th International Conference on Smart Instrumentation, Measurement and Applications, Bandung, Indonesia.

[img] PDF
Restricted to Registered users only

Download (344kB) | Request a copy
[img] PDF (SCOPUS) - Supplemental Material
Restricted to Registered users only

Download (266kB) | Request a copy


The algorithms based on image feature detection and matching are critical in the field of computer vision. Feature extraction and matching algorithms are used in many computer vision problems, including object recognition and structure from motion. Each feature detector and descriptor algorithm’s computational efficiency and robust performance have a major impact on image matching precision and time utilization. The performance of image matching algorithms that use expensive descriptors for detection and matching is addressed using existing approaches. The algorithm’s efficiency is measured by the number of matches found and the number of faults discovered when evaluated against a given pair of images. It also depends on the algorithm that detects the features and matches them in less amount of time. This paper examines and compares the different algorithm (SURF, ORB, BRISK, FAST, KAZE, MINEIGEN, MSER) performances using distinct parameters such as affine transformation, blur, scale, illumination, and rotation. The Oxford dataset is used to assess their robustness and efficiency against the parameters of interest. The time taken to detect features, the time taken to match images, the number of identified feature points, and the total running time is recorded in this study. The quantitative results show that the ORB and SURF algorithms detect and match more features than other algorithms. Furthermore, they are computationally less expensive and robust compared to other algorithms. In addition, the robustness of ORB and SURF is quite high in terms of outliers, and the amount of time taken to match with the reference is also significantly less. However, the efficiency of SURF reduces against blur transformation. FAST is good in detecting corners but lacks efficiency under different transformations. Experiments show that when each algorithm is subjected to numerous alterations, it has its own set of advantages and limitations.

Item Type: Conference or Workshop Item (Invited Papers)
Uncontrolled Keywords: ORB, SURF, FAST, Feature detection, Computer Vision, Augmented Reality
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Kulliyyah of Engineering
Depositing User: Prof. Dr. Teddy Surya Gunawan
Date Deposited: 14 Sep 2021 11:44
Last Modified: 12 Oct 2021 09:10
URI: http://irep.iium.edu.my/id/eprint/92220

Actions (login required)

View Item View Item


Downloads per month over past year