For a growing range of applications, deploying AI in the real world means running various models at the edge. Fortunately, powerful edge processors are increasingly capable of handling demanding AI workloads. But performance alone is not sufficient. To meet the […]
Traditionally, image sensors have been optimized to produce images that look natural to humans. For images consumed by algorithms, what matters is capturing the most information. We can achieve this via higher resolution, but higher resolution means lower sensitivity. To […]
The evolution of embedded vision and imaging technologies is enabling the development of powerful applications that would not have been practical previously. The possibilities seem to be endless. Yet, developing embedded vision solutions is challenging. There are many hardware and […]
Advances in image sensor capabilities, such as improved imaging in low-light conditions, coupled with reduced footprint and lower power consumption, are enabling more and more systems to incorporate computer vision. In this presentation we’ll illuminate the key image sensor specifications […]
Always-sensing cameras are becoming a common AI-enabled feature of consumer devices, much like the always-listening Siri or Google assistants. They can enable a more natural and seamless user experience, such as automatically locking and unlocking the device based on whether […]
Next-generation AI and computer vision applications for autonomous vehicles, cameras, drones and robots require higher-than-ever computing power. Often, the most efficient way to deliver high performance (especially in cost- and power-constrained applications) is to use multi-core processors. But developers must […]
As enterprises and consumers increasingly adopt machine learning-enabled smart cameras, the expectations of these end users are becoming more sophisticated. In particular, smart camera users increasingly expect their deployed cameras to improve over time—for example, becoming more accurate and gaining […]
Virtually all computer vision machine learning models involve classification—for example, “how many humans are in the frame?” To train such a model, you need examples of every class of object the model is intended to detect. But what happens when […]
Video is hard. Network Optix makes it really easy. Video has the potential to become a valuable source of operational data for business, especially with the help of AI. We’ll show how to practically build a video-enabled cloud or hybrid […]
NetsPresso is a development pipeline that enables developers to build, optimize and deploy vision AI models faster and better. Using conventional tools, it typically takes 6-12 weeks to select, train, optimize and deploy a vision DNN. Using NetsPresso, developers can […]
