Writing these kinds of vision programs is hard. Unless... you address the problem with some advanced machine learning techniques. Applying machine learning to visual problems is "the backbone" of what we do at vision.ai and computer vision research has been a personal passion of mine for over a decade. So I decided to take our most recent piece of vision tech for a spin. We try not to code while on vacation (a good team needs good rest), and I don't consider using our GUI-based VMX software as hardcore as "coding." Unlike traditional vision systems whose operation might leave you with an engineering-hangover, using VMX is more akin to playing Minecraft. I figured that playing a video game or two on vacation is permissible.
Eliminating the residual sunscreen from my hands, I rebooted my soul with an iced gulp of Spice Isle Coffee and fired up my trusty Macbook Pro. I then grabbed the first few vacation-themed objects from the kitchen. (And yes, I'm on vacation for Thanksgiving -- the objects include canned fruit, sunscreen, and a bottle of booze.) Then it was time to throw the barcode detection problem at VMX.
|Step 1: Barcode Initial Selections
Please note that he barcode is not actually "read" (so this program can't tell whether the region corresponds to canned pineapples or sunscreen), the region of interest is simply detected and tracked in real-time.
|Final Step: Tweaking Learned Positives and Negatives
To learn how to get your hands on VMX, sign up on the mailing list at http://vision.ai or if you're daring enough, you can purchase an early beta license key from https://beta.vision.ai.
So what's next? Should I build a boat detector? Maybe I should train a detector to let me know when I run low on Spice Isle Coffee? Or how about going on a field trip and counting bikinis on the beach?