NEW YORK–Twenty years ago, imaging/vision was a subject matter that only camera manufacturers cared about.
Today, imaging has become a decidedly hot topic among a diverse world of system engineers, including those who build automotive electronics, mobile handsets, tablets, PCs, digital TVs and medical devices.
Imaging is now giving a machine the ability to see and understand the world. Sure, it ain’t perfect, but it is improving.
What has changed today is that system vendors no longer use imaging or vision technology just to capture images for keeps’ sake. They see imaging as a fundamental tool to “extract meaning from pixels,” as Jeff Bier (left), president of Berkeley Design Technology Inc., likes to say.
Imaging/vision can help an embedded system to detect, identify and track a person or object; it can help a system to diagnose a person’s health or track his emotion. Imaging can certainly assist a driver in safer driving.
Imaging has always fascinated me, but the magnitude of a potential market for embedded vision didn’t really hit me until Tensilica earlier this week
announced its own imaging/video IP core, called IVP. Tensilica isn’t alone. The California-based company is actually following in the footsteps of Ceva, which
rolled out its own imaging IP core platform, called MM3101, a year ago.
You know how things go in the electronics world. Once chip companies start integrating a DSP IP core dedicated to imaging on an SoC, it suggests that system companies are damn sure wanting imaging solutions.
The mobile industry looking for embedded vision technology reminds me of the PC industry in 1990s.
Every time a new multimedia type--be it audio, 2-D/3-D graphics or video--popped up on the horizon for PCs, it drove the electronics industry to develop new chips, boards and eventually new PC architectures to accommodate the new multimedia type. That, in turn, generated fresh demand for new PCs.
Embedded vision technology is on the cusp of following a similar path. It’s positioned to drive a new generation of mobile handsets, tablets, digital cameras and automotive systems.
I understand how the embedded vision algorithms--originally derived from the field of computer vision or man-machine interface--are now coming downstream to handsets or any other embedded devices.
In fact, Ceva earlier this year released a
new computer vision software library for the development of vision-enabled applications targeting mobile, home, PC and automotive applications. This is based on OpenCV, a standard library of programming functions for computer vision processing that leverages decades of hard work.
What I don’t quite get is exactly what specific embedded vision applications are prompting mobile handsets and any other systems to crave for something like Tensilica’s IVP or Ceva’s imaging core, rather than just doing away with plain old multi-core CPUs.
I popped the question to Bier.
EE Times: Why would embedded systems guys go for a specialized imaging processor?
Bier: Multi-core CPUs are very powerful and programmable, but not very energy-efficient. So if you have a battery-powered device that is going to be doing a lot of vision processing, you may be motivated to run your vision algorithms on a more specialized processor.
EE Times: What are good app examples for a battery-powered device that does a lot of vision processing?
Bier: Well, there are many, some real today and others on the horizon:
Smartphones increasingly do vision processing for applications like driver safety (see iOnRoad), augmented reality (see Vuforia), visual search (see Google Goggles), and gesture user interfaces (see eyeSight Mobile Technologies).
Another is digital cameras and camcorders, which need more-sophisticated features and performance to justify their existence when smartphones are ubiquitous.
Everyone keeps talking about smartphones displacing cameras, but despite the fact that I’m wedded to my smartphone, I have four DSCs and a camcorder that I use regularly. You already know about features like face detection and smile detection. More sophisticated features are coming, like face recognition.
Next: Lower-power needs for ADAS
