At John Deere, we use machine learning and computer vision (including stereo vision) for challenging outdoor applications such as obstacle detection, vision-based guidance and weed management, among many others. The quality of the images our systems obtain, and the accuracy […]
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, we will explore likely future demand for automotive radars, […]
Being first to market can mean the difference between success and failure of a new product. But rapid product development brings challenges. With the growing use of AI in embedded vision applications, designers need more flexibility to change and refine […]
Vision applications often rely on object detectors, which determine the nature and location of objects in a scene. But many vision applications require a different type of visual understanding: semantic segmentation. Semantic segmentation classifies each pixel of an image, associating […]
When used correctly, transformer neural networks can deliver greater accuracy for less computation. But transformers are challenging for existing AI engine architectures because they use many compute functions not required by previously prevalent convolutional neural networks. In this talk, we […]
By taking advantage of select features in standardized interfaces, vision system architects can help reduce processor load, cost and power consumption while gaining flexibility to source components from multiple vendors. In this presentation, we’ll introduce the standardized MIPI CSI-2 imaging […]
The trend of pushing AI/ML capabilities to the edge brings design challenges around the need to combine high-performance computing (for AI/ML algorithms) with low power consumption (to enable battery-powered sensing systems). Designers are often faced with a choice between using […]
Today, a great deal of effort is wasted in optimizing and re-optimizing computer vision and machine learning application software as algorithms change and as developers target different processors. Developers need a way to deploy their applications on any processor, taking […]
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 […]
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 […]
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