We live in a three-dimensional world, and the ability to perceive in three dimensions is essential for many systems. In this talk, we will introduce the main types of depth cameras, including passive and active stereo; structured light; and direct […]
Fundamentals
Deep neural networks, widely used in computer vision tasks, require substantial computation and memory resources, making it challenging to run these models on resource-constrained devices. Quantization involves modifying DNNs to use smaller data types (e.g., switching from 32-bit floating-point values […]
Transformer neural networks have revolutionized artificial intelligence by introducing an architecture built around self-attention mechanisms. This has enabled unprecedented advances in understanding sequential data, such as human languages, while also dramatically improving accuracy on nonsequential tasks like object detection. In […]
In this presentation, we will introduce the essential features of cameras for embedded vision applications. We will explain lens mounts and camera interface options such as MIPI, USB and GigE. We’ll explore sensor features and trade-offs, including rolling shutter vs. […]
April 4, 2024
• mnakamura
Imagine a robot navigating a busy factory floor, a self-driving car detecting obstacles on the road or a security system accurately recognizing faces. These advancements in artificial intelligence are powered by tiny devices called CMOS image sensors. With a vast […]
Simultaneous localization and mapping (SLAM) is widely used in industry and has numerous applications where camera or ego-motion needs to be accurately determined. In this presentation, Amol Borkar and Shrinivas Gadkari will give an introduction to visual SLAM, explain how […]
March 21, 2024
• mnakamura
In this intermediate-level presentation, we will delve into some of the most common problems that arise when training deep neural networks. We’ll provide a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score. […]
March 21, 2024
• mnakamura
Object tracking is an essential capability in many computer vision systems, including applications in fields such as traffic control, self-driving vehicles, sports and more. In this session, we’ll walk through the construction of a typical multiple object tracking (MOT) algorithm […]
March 21, 2024
• mnakamura
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 […]
In this presentation we provide an introduction to radar (short for radio detection and ranging) for machine perception. Radar is a proven technology with a long history of successful development and it plays an increasingly important role in the deployment […]
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