Fergus Casey, R&D Director for ARC Processors at Synopsys, presents the “Designing Smarter, Safer Cars with Embedded Vision Using Synopsys EV Processor Cores” tutorial at the May 2018 Embedded Vision Summit. Consumers, the automotive industry and government regulators are requiring greater levels of automotive functional safety with each new generation of cars. Embedded vision, using […]
The Embedded Vision Summit was held on May 21-24, 2018 in Santa Clara, California, as an educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations delivered at the Summit are listed below. All of the slides from these presentations are included in… May 2018 Embedded Vision Summit
May 2018 Embedded Vision Summit Slides Read More +
The ability to "stitch" together (offline or in real-time) multiple images taken simultaneously by multiple cameras and/or sequentially by a single camera, in both cases capturing varying viewpoints of a scene, is becoming an increasingly appealing (if not necessary) capability in an expanding variety of applications. High quality of results is a critical requirement, one
Computer Vision in Surround View Applications Read More +
This technical article was originally published on Texas Instruments' website (PDF). It is reprinted here with the permission of Texas Instruments. Introduction Cameras are the most precise mechanisms used to capture accurate data at high resolution. Like human eyes, cameras capture the resolution, minutiae and vividness of a scene with such beautiful detail that no
Stereo Vision: Facing the Challenges and Seeing the Opportunities for ADAS Applications Read More +
Minyoung Kim, Senior Research Engineer at Panasonic Silicon Valley Laboratory, presents the "A Fast Object Detector for ADAS using Deep Learning" tutorial at the May 2017 Embedded Vision Summit. Object detection has been one of the most important research areas in computer vision for decades. Recently, deep neural networks (DNNs) have led to significant improvement
“A Fast Object Detector for ADAS using Deep Learning,” a Presentation from Panasonic Read More +
Luca Rigazio, Director of Engineering for the Panasonic Silicon Valley Laboratory, presents the "Unsupervised Everything" tutorial at the May 2017 Embedded Vision Summit. The large amount of multi-sensory data available for autonomous intelligent systems is just astounding. The power of deep architectures to model these practically unlimited datasets is limited by only two factors: computational
“Unsupervised Everything,” a Presentation from Panasonic Read More +
Aman Sikka, Vision System Architect at Pearl Automation, presents the "Designing a Vision-based, Solar-powered Rear Collision Warning System" tutorial at the May 2017 Embedded Vision Summit. Bringing vision algorithms into mass production requires carefully balancing trade-offs between accuracy, performance, usability, and system resources. In this talk, Sikka describes the vision algorithms along with the system
Unmesh Bordoloi, Senior Researcher at General Motors, presents the "Collaboratively Benchmarking and Optimizing Deep Learning Implementations" tutorial at the May 2017 Embedded Vision Summit. For car manufacturers and other OEMs, selecting the right processors to run deep learning inference for embedded vision applications is a critical but daunting task. One challenge is the vast number
Jayachandra Dakala, Technical Architect at PathPartner Technology, presents the "Approaches for Vision-based Driver Monitoring" tutorial at the May 2017 Embedded Vision Summit. Since many road accidents are caused by driver inattention, assessing driver attention is important to preventing accidents. Distraction caused by other activities and sleepiness due to fatigue are the main causes of driver
Mark Bünger, VP of Research at Lux Research, presents the "Automakers at a Crossroads: How Embedded Vision and Autonomy Will Reshape the Industry" tutorial at the May 2017 Embedded Vision Summit. The auto and telecom industries have been dreaming of connected cars for twenty years, but their results have been mediocre and mixed. Now, just
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