Following through part of, then skimming the rest of, the first section of PyImageSearch Getting Started guide taught me there’s a lot of fascinating information here. Certainly more than enough for me to know I’ll be returning and consult as I tackle project ideas in the future. For now I wanted to skimp through the rest and note the problem areas it covers.
The Deep Learning section immediately follows the startup section, because they’re a huge part of recent advancements in computer vision. Like most tutorials I’ve seen on Deep Learning, this section goes through how to set up and train a convolutional neural network to act as an image classifier. Discussions about training data, tuning training parameters, and applications are built around these tasks.
After the Deep Learning section are several more sections, each focused on a genre of popular applications for machine vision.
- Face Applications start from recognizing the presence of human faces to recognizing individual faces and applications thereof.
- Optical Character Recognition (OCR) helps a computer read human text.
- Object detection is a more generalized form of detecting faces or characters, and there’s a whole range of tools. This will take time to learn in order to know which tools are the right ones for specific jobs.
- Object tracking: once detected, sometimes we want an object tracked.
- Segmentation: Detect objects and determine which pixels are and aren’t part of that object.
To deploy algorithms described above, the guide then talks about hardware. Apart from theoretical challenges, there’s also hardware constraint that are especially acute on embedded hardware like Raspberry Pi, Google Coral, etc.
After hardware, there are a few specific application areas. From medical computer vision, to video processing, to image search engine.
This is an impressively comprehensive overview of computer vision. I think it’ll be a very useful resource for me in the future, as long as I keep in mind a few characteristics of this site.