May 2017 Embedded Vision Summit Replay

Conference Presentation Slides

2017 Embedded Vision Summit Slides (214 MB—click to download.)

Conference Overview Presentations

	Welcome Remarks and Conference Overview (Day 1) Jeff Bier, Embedded Vision Alliance
	Welcome Remarks and Conference Overview (Day 2) Jeff Bier, Embedded Vision Alliance

Keynote Presentation

"Deep Visual Understanding from Deep Learning" Professor Jitendra Malik, University of California, Berkeley

Business Insights Track Presentations

	"Video Cameras Without Video: Opportunities For Sensing With Embedded Vision" Michael Tusch
	"Designing Vision Systems for Human Operators and Workflows: A Case Study" Arun Chhabra, 8tree
	"This Changes Everything — Why Computer Vision Will Be Everywhere" Tim Ramsdale, ARM
	"How 3D Maps Will Change the World" Vitaliy Goncharuk, Augmented Pixels
	"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring" Carter Maslan, Camio
	"Intelligent Video Surveillance: Are We There Yet?" Nik Gagvani, CheckVideo
	"How to Start an Embedded Vision Company" Chris Rowen, Cognite Ventures
	"1,000X in Three Years: How Embedded Vision is Transitioning from Exotic to Everyday" Jeff Bier, Embedded Vision Alliance and BDTI
	"The Rapid Evolution and Future of Machine Perception" Jay Yagnik, Google
	"Vision for All?" Jeff McVeigh, Intel
	"Using Markerless Motion Capture to Win Baseball Games" Steven Cadavid, KinaTrax
	"The Coming Shift from Image Sensors to Image Sensing" Paul Gallagher, LG
	"Automakers at a Crossroads: How Embedded Vision and Autonomy Will Reshape the Industry" Mark Bünger, Lux Research
	"Another Set of Eyes: Machine Vision Automation Solutions for In Vitro Diagnostics" Sadie Zeller, Microscan Systems
	"Using Satellites to Extract Insights on the Ground" Boris Babenko, Orbital Insight
	"Enabling the Full Potential of Machine Learning" Derek Meyer, Wave Computing
	"What’s Hot? The M&A and Funding Landscape for Computer Vision Companies" Randy Burger, Woodside Capital Partners
	Vision Entrepreneurs' Panel
	Vision Tank Competition Finalist Presentations Always Innovating, Imagry, Lucid VR, Machines with Vision, and Perceptin

Enabling Technologies Track Presentations

	"A Multi-purpose Vision Processor for Embedded Systems" Michael Melle and Felix Nikolaus, Allied Vision
	"A New Generation Vision Camera for Embedded Systems" Paul Maria Zalewski, Allied Vision
	"Computer Vision on ARM: The Spirit Object Detection Accelerator" Tim Hartley, ARM
	"Computer Vision on ARM: Faster Ways to Optimize Software for Advanced Mobile Computing Platforms" Roberto Mijat, ARM
	"Scalable Neural Network Processors for Embedded Applications" Pulin Desai, Cadence
	"Fast Inference in Low Power Systems via CEVA's Deep Neural Network Solution" Yair Siegel, CEVA
	"Developing Real-time Video Applications with CoaXPress" Jean-Michel Wintgens, Euresys
	"Ultra-Efficient VPU: Low-power Deep Learning, Computer Vision and Computational Photography" Petronel Bigioi, FotoNation
	"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP" Pete Warden, Google
	"How Intel’s Latest RealSense Technology Can Help Your Embedded Systems See, Navigate, and Understand the Real World" Anders Grunnet-Jepsen, Intel
	"Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs" Avinash Nehemiah and Girish Venkataramani, MathWorks
	"Embedded Vision Made Smart: Introduction to the HALCON Embedded Machine Vision Library" Olaf Munkelt, MVTec
	"Always-on Vision Becomes a Reality" Evgeni Gousev, Qualcomm Research
	"Computer Vision and Machine Learning at the Edge" Michael Mangan, Qualcomm Technologies
	"Designing Scalable Embedded Vision SoCs from Day 1" Pierre Paulin, Synopsys
	"The Power of a Turnkey Camera Solution: Introduction to the Camera Development Kit" Fan Wang, Thundersoft
	"New Dataflow Architecture for Machine Learning" Chris Nicol, Wave Computing
	"Caffe to Zynq: State-of-the-Art Machine Learning Inference Performance in Less Than 5 Watts" Vinod Kathail, Xilinx
	"OpenCV on Zynq: Accelerating 4k60 Dense Optical Flow and Stereo Vision" Nick Ni, Xilinx
	"PCI Express – A High-bandwidth Interface for Multi-camera Embedded Systems" Max Larin, XIMEA

Fundamentals Track Presentations

	"Time of Flight Sensors: How Do I Choose Them and How Do I Integrate Them?" Mark Hebbel, Basler
	"Demystifying Deep Neural Networks" Shehrzad Qureshi, BDTI
	"How to Choose a 3D Vision Technology" Chris Osterwood, Carnegie Robotics
	"A Shallow Dive into Training Deep Neural Networks" Sammy Sidhu, DeepScale
	"Introduction to Optics for Embedded Vision" Jessica Gehlhar, Edmund Optics
	"Unsupervised Everything" Luca Rigazio, Panasonic
	"How CNNs Localize Objects with Increasing Precision and Speed" Auro Tripathy, ShatterLine Labs

Technical Insights Track Presentations

	"Adventures in DIY Embedded Vision: The Can’t-miss Dartboard" Mark Rober
	"Designing CNN Algorithms for Real-time Applications" Matthew Chiu, Almond AI
	"How Image Sensor and Video Compression Parameters Impact Vision Algorithms" Ilya Brailovskiy, Amazon Lab126
	"Making Cozmo See" Andrew Stein, Anki
	"Techniques to Reduce Power Consumption in Embedded DNN Implementations" Samer Hijazi, Cadence
	"The OpenVX Computer Vision Library Standard for Portable, Efficient Code" Frank Brill, Cadence and the Khronos Group
	"Designing a Stereo IP Camera From Scratch" Anton Leontiev, ELVEES, JSC
	"Collaboratively Benchmarking and Optimizing Deep Learning Implementations" Unmesh Bordoloi, General Motors
	"Training CNNs for Efficient Inference" Paul Brasnett, Imagination Technologies
	"Edge Intelligence: Visual Reinforcement Learning for Mobile Devices" Adham Ghazali, Imagry
	"Designing a Wearable Imaging Device – for Mice" Shung Chieh, Inscopix
	"Designing Deep Neural Network Algorithms for Embedded Devices" Minje Park, Intel
	"Making OpenCV Code Run Fast" Vadim Pisarevsky, Intel
	"The Battle Between Traditional Algorithms and Deep Learning: The 3 Year Horizon" Cormic Brick, Intel (Movidius Group)
	"The Vision Acceleration API Landscape: Options and Trade-offs" Neil Trevett, Khronos Group and NVIDIA
	"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" Alexey Rybakov, Luxoft
	"Performing Multiple Perceptual Tasks With a Single Deep Neural Network" Andrew Rabinovich, Magic Leap
	"How to Test and Validate an Automated Driving System" Avinash Nehemiah, MathWorks
	"Designing and Implementing Camera ISP Algorithms Using Deep Learning and Computer Vision" Val Marchevsky, Motorola
	"Implementing an Optimized CNN Traffic Sign Recognition Solution" Rafal Malewski, NXP Semiconductors Sébastien Taylor, Au-Zone Technologies
	"Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles" Ali Osman Ors, NXP Semiconductors
	"A Fast Object Detector for ADAS using Deep Learning" Minyoung Kim, Panasonic
	"Approaches for Vision-based Driver Monitoring" Dakala Jayachandra, PathPartner Technology
	"Designing a Vision-based, Solar-powered Rear Collision Warning System" Aman Sikka, Pearl Automation
	"Vision Challenges in a Robotic Power Tool" Alec Rivers, Shaper Tools
	"Image Sensor Formats and Interfaces for IoT Applications" Tatsuya Sugioka, Sony
	"Moving CNNs from Academic Theory to Embedded Reality" Tom Michiels, Synopsys
	"End to End Fire Detection Deep Neural Network Platform" Divya Jain, Tyco Innovation
	"Computer-vision-based 360-degree Video Systems: Architectures, Algorithms and Trade-offs" Marco Jacobs, videantis

Technology Showcase Demonstrations

	3M
	AImotive
	Aldec
	Aldec
	Allied Vision
	Allied Vision
	Anki
	ARM
	ARM
	ARM
	ARM
	Au-Zone Technologies
	Avnet
	BDTI
	BDTI
	BDTI
	Cadence
	Cadence
	CEVA
	CEVA
	Imagination Technologies
	Imagination Technologies
	Intel
	Intel
	Intel
	Intel
	Intel
	Intel
	Intel
	Intel
	Lucid VR
	Luxoft
	Luxoft
	Machines With Vision
	NET
	Nextchip
	Nextchip
	NXP Semiconductors
	NXP Semiconductors
	Pearl Automation
	PerceptIn
	Shaper Tools
	Synopsys
	Synopsys
	Thundersoft
	Xilinx
	XIMEA