Presentation and code for NDC Oslo 2014.

With a camera and a powerful computer in every pocket, the shift from typing to computer vision is just about to happen.

Instead of entering information about a wine for our tasting notes, we snap a photo and let the computer recognize it and look up the information. Instead of having the shopper enter his credit card info, we can read the information straight from the card with the camera. We can even use computer vision to play a fanfare when someone arrives at our doorstep carrying a pizza box.

The tools to make this happen are in the field of computer vision. Today, the are solid techniques and cross-platform open-source libraries such as OpenCV available that make it easy to build this into everyday applications.

This presentation will show you how.

We will look at practical applications of computer vision such as using the camera to scan text (OCR), reading the info from a credit card or a license plate from a passing car, recognizing the pizza box in the image or using the wine label as a visual query against our wine database to find the producer, vintage, grapes and other information with no manual data entry.

The talk combines practical examples with a presentation of the basic image processing techniques that make it possible.

You will learn about basic image transformations, how to find interesting or characteristic parts of image, extract and recognize text and and ways to compare images and how to recognize known shapes and objects in images.

Be prepared for a highly visual talk that provides not only an introduction to making the computer see but also presents its mathematical and statistical machine learning underpinnings in a very practical context.

Developers curious about how to max out the CPU and camera on their smartphone while saving their users a lot of trouble. Entry level talk: No Ph.D., machine learning or computer vision background required.

Martin Jul is a developer from Copenhagen, Denmark working in startup stealth mode in the field of computer vision and machine learning.

Some time before the Internet Age he studied mathematics and computer science in Copenhagen, and he is very happy that libraries and powerful processors are finally making the hitherto exotic and inaccesible academic disciplines involved computing with images accessible to everyone.