Master Assignment
[B] Discovery of missing objects in predefined scene images
Type: Master EE/CS/HMI
Period: TBD
Student: (Unassigned)
If you are interested, please contact :
Project Background:
In many real-world scenarios, the ability to detect missing objects in images is crucial, from ensuring inventory completeness in warehouses to monitoring equipment in medical or industrial settings. This project focuses on leveraging machine learning and computer vision to automatically identify missing objects in predefined scenes. By developing robust algorithms capable of recognizing when something is absent, this research can enhance automated systems, improve efficiency, and prevent errors in various fields such as retail, healthcare, and manufacturing.
This MSc project is part of a broader project and can focus on one or several of the following objectives:
- Build and train a machine learning model capable of recognizing missing objects in images of predefined scenes.
- Develop a dataset of controlled environments where objects are either present or missing to train the system.
- Tackle challenges like occlusions, similar object shapes, and varying lighting conditions in real-world image data.
- Create an efficient framework for practical applications in inventory management, quality control, and safety monitoring.
Why Join?
- Be part of an innovative project applying AI to solve practical challenges in object recognition and scene understanding.
- Gain experience in dataset creation, model training, and deploying machine learning solutions for real-world applications.
- Collaborate with experts in machine learning and computer vision, contributing to technologies that improve accuracy and efficiency across various industries.
Who Should Apply?
- Students with background in machine learning and computer vision.
Those interested in applying AI to solve problems related to object recognition and scene understanding.