Cameras and Sensors for Embedded Vision


While analog cameras are still used in many vision systems, this section focuses on digital image sensors—usually either a CCD or CMOS sensor array that operates with visible light. However, this definition shouldn’t constrain the technology analysis, since many vision systems can also sense other types of energy (IR, sonar, etc.).

The camera housing has become the entire chassis for a vision system, leading to the emergence of “smart cameras” with all of the electronics integrated. By most definitions, a smart camera supports computer vision, since the camera is capable of extracting application-specific information. However, as both wired and wireless networks get faster and cheaper, there still may be reasons to transmit pixel data to a central location for storage or extra processing.

A classic example is cloud computing using the camera on a smartphone. The smartphone could be considered a “smart camera” as well, but sending data to a cloud-based computer may reduce the processing performance required on the mobile device, lowering cost, power, weight, etc. For a dedicated smart camera, some vendors have created chips that integrate all of the required features.


Until recent times, many people would imagine a camera for computer vision as the outdoor security camera shown in this picture. There are countless vendors supplying these products, and many more supplying indoor cameras for industrial applications. Don’t forget about simple USB cameras for PCs. And don’t overlook the billion or so cameras embedded in the mobile phones of the world. These cameras’ speed and quality have risen dramatically—supporting 10+ mega-pixel sensors with sophisticated image processing hardware.

Consider, too, another important factor for cameras—the rapid adoption of 3D imaging using stereo optics, time-of-flight and structured light technologies. Trendsetting cell phones now even offer this technology, as do latest-generation game consoles. Look again at the picture of the outdoor camera and consider how much change is about to happen to computer vision markets as new camera technologies becomes pervasive.


Charge-coupled device (CCD) sensors have some advantages over CMOS image sensors, mainly because the electronic shutter of CCDs traditionally offers better image quality with higher dynamic range and resolution. However, CMOS sensors now account for more 90% of the market, heavily influenced by camera phones and driven by the technology’s lower cost, better integration and speed.

“Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product,” a Presentation from Cocoon Health

Pavan Kumar, Co-founder and CTO of Cocoon Cam (formerly Cocoon Health), delivers the presentation “Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product” at the Embedded Vision Alliance’s September 2019 Vision Industry and Technology Forum. Kumar explains how his company is evolving its use of edge and cloud vision computing in continuing to bring new

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“Selecting the Right Imager for Your Embedded Vision Application,” a Presentation from Capable Robot Components

Chris Osterwood, Founder and CEO of Capable Robot Components, presents the "Selecting the Right Imager for Your Embedded Vision Application" tutorial at the May 2019 Embedded Vision Summit. The performance of your embedded vision product is inexorably linked to the imager and lens it uses. Selecting these critical components is sometimes overwhelming due to the

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“Machine Learning at the Edge in Smart Factories Using TI Sitara Processors,” a Presentation from Texas Instruments

Manisha Agrawal, Software Applications Engineer at Texas Instruments, presents the "Machine Learning at the Edge in Smart Factories Using TI Sitara Processors" tutorial at the May 2019 Embedded Vision Summit. Whether it’s called “Industry 4.0,” “industrial internet of things” (IIOT) or “smart factories,” a fundamental shift is underway in manufacturing: factories are becoming smarter. This

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“Introduction to Optics for Embedded Vision,” a Presentation from Jessica Gehlhar

Jessica Gehlhar, formerly an imaging engineer at Edmund Optics, presents the "Introduction to Optics for Embedded Vision" tutorial at the May 2019 Embedded Vision Summit. This talk provides an introduction to optics for embedded vision system and algorithm developers. Gehlhar begins by presenting fundamental imaging lens specifications and quality metrics such as MTF. She explains

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“Improving the Safety and Performance of Automated Vehicles Through Precision Localization,” a Presentation from VSI Labs

Phil Magney, founder of VSI Labs, presents the “Improving the Safety and Performance of Automated Vehicles Through Precision Localization” tutorial at the May 2019 Embedded Vision Summit. How does a self-driving car know where it is? Magney explains how autonomous vehicles localize themselves against their surroundings through the use of a variety of sensors along

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“Distance Estimation Solutions for ADAS and Automated Driving,” a Presentation from AImotive

Gergely Debreczeni, Chief Scientist at AImotive, presents the "Distance Estimation Solutions for ADAS and Automated Driving" tutorial at the May 2019 Embedded Vision Summit. Distance estimation is at the heart of automotive driver assistance systems (ADAS) and automated driving (AD). Simply stated, safe operation of vehicles requires robust distance estimation. Many different types of sensors

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“MediaTek’s Approach for Edge Intelligence,” a Presentation from MediaTek

Bing Yu, Senior Technical Manager and Architect at MediaTek, presents the "MediaTek’s Approach for Edge Intelligence" tutorial at the May 2019 Embedded Vision Summit. MediaTek has incorporated an AI processing unit (APU) alongside the traditional CPU and GPU in its SoC designs for the next wave of smart client devices (smartphones, cameras, appliances, cars, etc.).

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May 18 - 21, Santa Clara, California

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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