I just started learning about machine learning recently and have a project where I have to develop a program for QR code localization so that a QR code can be detected and read at any angle of rotation. Development will be done in Python.The plan is to gather various images of the QR codes at different angles with different backgrounds. From this I would like to create a dataset for training with neural networks and then testing. The issue that I'm having is that I can't seem to figure out a correct feature design for the dataset and how to identify the QR code from the images for feature processing. Would I use ground-truth images or edge magnitude maps?Any help with this would be amazing?Thanks for your time.
Feature selection - QR code localization
machine learning;classification;feature selection;feature extraction;supervised learning