Edge AI and Vision Alliance

FEATURED VIDEOS "Tailoring Convolutional Neural Networks for Low-Cost, Low-Power Implementation," a Presentation from Synopsys Deep learning-based object detection using convolutional neural networks (CNN) has recently emerged as one of the leading approaches for achieving state-of-the-art detection accuracy for a wide range of object classes. Most of the current CNN-based detection algorithm implementations run on high-performance […]

FEATURED VIDEOS "3D from 2D: Theory, Implementation, and Applications of Structure from Motion," a Presentation from videantis Structure from motion uses a unique combination of algorithms that extract depth information using a single 2D moving camera. Using a calibrated camera, feature detection, and feature tracking, the algorithms calculate an accurate camera pose and a 3D

FEATURED VIDEOS Combining Flexibility and Low-Power in Embedded Vision Subsystems: An Application to Pedestrian Detection Bruno Lavigueur, Embedded Vision Subsystem Project Leader at Synopsys, presents a case study of a pedestrian detection application. Starting from a high-level functional description in OpenCV, he decomposes and maps the application onto a heterogeneous platform consisting of a high-performance

FEATURED VIDEOS "Large-Scale Deep Learning for Building Intelligent Computer Systems," a Keynote Presentation from Google Jeff Dean, Senior Fellow at Google, presents the keynote talk, "Large-Scale Deep Learning for Building Intelligent Computer Systems," at the May 2016 Embedded Vision Summit. Over the past few years, Google has built two generations of large-scale computer systems for

FEATURED VIDEOS "Challenges in Object Detection on Embedded Devices," a Presentation from CEVA As more products ship with integrated cameras, says Adar Paz, Imaging and Computer Vision Team Leader at CEVA, there is an increased potential for computer vision (CV) to enable innovation. For instance, CV can tackle the "scene understanding" problem by first figuring