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Context-Aware Image Processing for Mobile Applications

Primary supervisor

Pari Delir Haghighi

Co-supervisors

  • Dr Yuxin Zhang (Monash Engineering)
  • A/Prof Prem Jayaraman (Swinburne University)
  • Dr Abdur Forkan (Swinburne University)

Mobile applications that use image processing for object recognition have recently gained popularity in a variety of domains such as agriculture and healthcare. Examples include plant disease detection apps or food object detection apps that aim to provide users with real-time and reliable information and advice. However, performing image processing onboard smartphones challenges and exhausts limited computational resources of the phone and can quickly drain the battery. 

Student cohort

Single Semester
Double Semester

Aim/outline

This project proposes a context-aware image processing approach for mobile applications that aims to improve the performance and the cost-efficiency of image processing operations.

URLs/references

  1. Knez, S., & Šajn, L. (2020). Food object recognition using a mobile device: Evaluation of currently implemented systems. Trends in Food Science & Technology, 99, 460-471.
  2. Cantillo, D., Cervantes, B., & Cardona, J. (2021, March). HealthCam: Machine Learning Models on Mobile Devices for Unhealthy Packaged Food Detection and Classification. In 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM) (pp. 1-6). IEEE.

Required knowledge

  • Mobile app development 
  • Basic image processing skills