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Introduction to Embedded Vision and the OpenCV Library (Embedded.com Article)

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By Eric Gregori
Senior Software Engineer
BDTI
This article was originally published at EE Times' Embedded.com Design Line. It is reprinted here with the permission of EE Times.

The term “embedded vision” refers to the use of computer vision technology in embedded systems. Stated another way, “embedded vision” refers to embedded systems that extract meaning from visual inputs.

Similar to the way that wireless communication has become pervasive over the past 10 years, embedded vision technology is poised to be widely deployed in the next 10 years. Vision algorithms were originally only capable of being implemented on costly, bulky, power-hungry computer systems, and as a result computer vision has mainly been confined to a few application areas, such as factory automation and military equipment.

Today, however, a major transformation is underway. Due to the emergence of very powerful, low-cost, and energy-efficient processors, image sensors, memories and other ICs, it has become possible to incorporate vision capabilities into a wide range of embedded systems.

Similarly, OpenCV, a library of computer vision software algorithms originally designed for vision applications and research on PCs has recently expanded to support embedded processors and operating systems.

Intel started OpenCV in the mid 1990s as a method of demonstrating how to accelerate certain algorithms. In 2000, Intel released OpenCV to the open source community as a beta version, followed by v1.0 in 2006. Robot developer Willow Garage, founded in 2006, took over support for OpenCV in 2008 and immediately released v1.1. The company has been in the news a lot lately, subsequent to the unveiling of its PR2 robot.

OpenCV v2.0, released in 2009, contained many improvements and upgrades. Initially, the majority of algorithms in the OpenCV library were written in C, and the...