Computer vision systems aren’t necessarily restricted to solely analyzing the portion of the electromagnetic spectrum that is visually perceivable by humans. Expanding the analysis range to encompass the infrared and/or ultraviolet spectrum, either broadly or selectively and either solely or in conjunction with visible spectrum analysis, can be of great benefit in a range of […]
Beyond-visible Light Applications in Computer Vision Read More +
This blog post was originally published by Twisthink. It is reprinted here with the permission of Twisthink. The exciting world of embedded cameras is experiencing rapid growth. Digital-imaging technology is being integrated into a wide range of new products and systems. Embedded cameras are becoming widely adopted in the automotive market, security and surveillance markets,
Use a Camera Model to Accelerate Camera System Design Read More +
In order for industrial automation systems to meaningfully interact with the objects they're identifying, inspecting and assembling, they must be able to see and understand their surroundings. Cost-effective and capable vision processors, fed by depth-discerning image sensors and running robust software algorithms, continue to transform longstanding industrial automation aspirations into reality. And, with the emergence
Visual Intelligence Opportunities in Industry 4.0 Read More +
This technical article was originally published at Synopsys' website. It is reprinted here with the permission of Synopsys. Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it
The Evolution of Deep Learning for ADAS Applications Read More +
Pete Warden, Google Research Engineer and technical lead on the company's mobile/embedded TensorFlow team, is a long-time advocate of the Embedded Vision Alliance. Warden has delivered presentations at both the 2016 ("TensorFlow: Enabling Mobile and Embedded Machine Intelligence") and 2017 ("Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP") Embedded Vision Summits, along with
Building Mobile Apps with TensorFlow: An Interview with Google’s Pete Warden Read More +
This article provides both background and implementation-detailed information on software frameworks and toolsets for deep learning-based vision processing, an increasingly popular and robust alternative to classical computer vision algorithms. It covers the leading available software framework options, the root reasons for their abundance, and guidelines for selecting an optimal approach among the candidates for a
Software Frameworks and Toolsets for Deep Learning-based Vision Processing Read More +
Although incorporating visual intelligence in your next product is an increasingly beneficial (not to mention practically feasible) decision, how to best implement this intelligence is less obvious. Image processing can optionally take place completely within the edge device, in a network-connected cloud server, or subdivided among these locations. And at the edge, centralized and distributed
This technical article was originally published at Basler's website. It is reprinted here with the permission of Basler. A status report with a focus on deep learning and Convolutional Neural Networks (CNNs) What are neural networks and why are they such a topic of interest for industrial image processing? They eliminate the need for developers
Are Neural Networks the Future of Machine Vision? Read More +
This article was originally published at the 2017 Embedded World Conference. With the emergence of increasingly capable processors, image sensors, and algorithms, it's becoming practical to incorporate computer vision capabilities into a wide range of systems, enabling them to analyze their environments via video inputs. This article explores the opportunity for embedded vision, compares various
The Internet of Things That See: Opportunities, Techniques and Challenges Read More +
This article explains the differences between images intended for human viewing and for computer analysis, and how these differences factor into the hardware and software design of a camera intended for computer vision applications versus traditional still and video image capture. It discusses various methods, both industry standard and proprietary, for assessing and optimizing computer
Image Quality Analysis, Enhancement and Optimization Techniques for Computer Vision Read More +
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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