Introduction To Computer Vision Using OpenCV (Article)
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By Eric Gregori
Senior Software Engineer and Embedded Vision Specialist
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.
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.
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...