Object detection using SIFT

Master thesis (2022)

Authors

Y. Tjiam Mechanical Engineering

Contributors

J.J. van den Dobbelsteen Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering (mentor)
R.M. Butler Medical Instruments & Bio-Inspired Technology - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2022 Yuan Tjiam
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Published Date

15-08-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

Surgical teams use instrument counts to prevent leaving unintended objects in patients. This is done manually, but could potentially be done through computer vision software. This paper presents a proof of concept for detecting instruments in the operating room with the Scale Invariant Feature Transform (SIFT). The SIFT algorithm is explored and tested on a variety of household appliances to substitute medical instruments. The algorithm responds differently to metal objects compared to matte objects and has room for many improvements. Further research on run time and multi object images is necessary. The proof of concept is considered successful when not taking run time into account.