The Embedded Vision Summit was held on September 19, 2012 in Boston, Massachusetts, as a technical educational forum for engineers interested in incorporating visual intelligence into electronic systems and software.
The program for the event included the following presentations, whose PDF-formatted foilsets are available for download as a consolidated ZIP file:
- Morning Keynote Presentation: "Emotion Technology: Enabling Machines to Understand How We Feel," Professor Rosalind Picard, MIT
- "Introduction to Embedded Vision," Jeff Bier, Embedded Vision Alliance
Session 1: "Embedded Vision Applications and Algorithms"
- "Image Processing for Object Recognition and Tracking," Dan Isaacs, Xilinx, and Michael Fawcett, iVeia
- "Moving Object Segmentation for Security and Surveillance Applications," Goksel Dedeoglu, Texas Instruments
- "Automotive Safety Applications and Algorithms," Gabby Yi, Analog Devices
- Afternoon Keynote Presentation: "OpenCV: Past, Present, and Future", Gary Bradski, OpenCV Foundation
Session 2: "Using Processors for Embedded Vision"
- "Harnessing Hardware Accelerators to Move from Algorithms to Embedded Vision," Michael Tusch, Apical
- "Using FPGAs to Accelerate Embedded Vision Applications," Kamalina Srikant, National Instruments
- "Challenges and Techniques in Using CPUs and GPUs for Embedded Vision," Ken Lee, VanGogh Imaging
Session 3: "Using Tools, APIs and Design Techniques for Embedded Vision"
- "Optimization and Acceleration for OpenCV-based Embedded Vision Applications," Bo Wu, Ph.D., Synopsys
- "Addressing System Design Challenges in Embedded Vision," Mario Bergeron, Avnet
- "Challenges and Techniques in Low-Cost, Low-Power, Small Form Factor Vision Applications," Simon Morris, CogniVue
- "Image Sensor Options and Trends for Embedded Vision," Eric Gregori, BDTI
Please also reference the Video Interviews & Demos section of this website for video recordings of the above presentations, which will become available as they are edited post-event.
Download "BostonSummit2012-Proceedings.zip" (39.2 MBytes).