CODEWITHSUNDEEP
pythonmachine-learningfeature-detectionfeature-extractionsupervised-learning
user2806570
user2806570

Reputation: 859

Machine Learning - Features design for Images

I have a machine-learning 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 to isolate the QR code or edge magnitude maps? Feature design for images seems to confuse me.

Upvotes: 1

Views: 248

Answers (1)

Atilla Ozgur
Atilla Ozgur

Reputation: 14701

You mention that you want to train neural networks. Instead of starting with your problem, start with a beginner example.

  1. Start with MNIST example for deep learning.
  2. Train your Neural Network on notMNIST dataset that is used in Udacity Deep Learning Course.

In these two examples, you will see that you do not design features but NN somehow founds correct features. Easiest solution would be to use same technique for QR codes in your dataset.

Upvotes: 2

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