Bookmark and Share

Introduction To Computer Vision Using OpenCV (Article)

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:


By Eric Gregori
Senior Software Engineer and Embedded Vision Specialist
BDTI

The name OpenCV has become synonymous with computer vision, but what is OpenCV? OpenCV is a collection of software algorithms put together in a library to be used by industry and academia for computer vision applications and research (Figure 1). OpenCV started at Intel in the mid 1990s as a method to demonstrate how to accelerate certain algorithms in hardware. In 2000, Intel released OpenCV to the open source community as a beta version, followed by v1.0 in 2006. In 2008, Willow Garage took over support for OpenCV and immediately released v1.1.

Introduction To OpenCV Figure 1

Figure 1: OpenCV, an algorithm library (courtesy Willow Garage)

Willow Garage dates from 2006. The company has been in the news a lot lately, subsequent to the unveiling of its PR2 robot (Figure 2). Gary Bradski began working on OpenCV when he was at Intel; as a senior scientist at Willow Garage he aggressively continues his work on the library.

Introduction To OpenCV Figure 2

Figure 2: Willow Garage's PR2 robot

OpenCV v2.0, released in 2009, contained many improvements and upgrades. Initially, OpenCV was primarily a C library. The majority of algorithms were written in C, and the primary method of using the library was via a C API. OpenCV v2.0 migrated towards C++ and a C++ API. Subsequent versions of OpenCV added Python support, along with Windows, Linux, iOS and Android OS support, transforming OpenCV (currently at v2.3) into a cross-platform tool. OpenCV v2.3 contains more than 2500 algorithms; the original OpenCV only had 500. And to assure quality, many of the algorithms provide their own unit tests.

So, what can you do with OpenCV v2.3? Think of OpenCV as a box of 2500 different...