Augmented Pixels: Indoor Navigation Platform for Drones

Drones are notoriously difficult to handle indoors: hard to control and prevent crashing into walls or people.

Augmented Pixels has been actively developing technology (including SLAM) to ensure safe flights as well as intuitive and easy navigation using Augmented Reality.

They came up with a platform that significantly reduces accident rates and minimizes the effect of "human factor". Moreover, it is possible to program the drone to fly around and land by itself.

The prospects for this technology include a wide range of use cases (e.g. inspection of premises for security, creation of 360-degree tours, etc.).Augmented Pixels is located in Palo Alto, CA. 

http://augmentedpixels.com

OpenRTM-aist official website | OpenRTM-aist

http://www.openrtm.org/


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Fwd: Visual Intelligence Through Deep Learning; Open UC Santa Cruz Faculty Position; More

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Begin forwarded message:

From: "Embedded Vision Insights from the Embedded Vision Alliance" <newsletter@embeddedvisioninsights.com>
Date: August 25, 2015 at 10:53:20 PM GMT+8
To: john.sokol@gmail.com
Subject: Visual Intelligence Through Deep Learning; Open UC Santa Cruz Faculty Position; More

VOL. 5, NO. 15

A NEWSLETTER FROM THE EMBEDDED VISION ALLIANCE

Late August 2015

IN THIS EDITION

New Content from the Embedded Vision Summit and Alliance Member Meeting Embedded Lucas-Kanade Tracking: Theory and Implementation Has Neural Network Processors' Time Come? Embedded Vision Community Conversations Embedded Vision in the News To view this newsletter online, please click here

LETTER FROM THE EDITOR

Dear Colleague, The Alliance continues to publish videos of great presentations from May's Embedded Vision Summit. Make sure you check out, for example, the highly rated keynote "Enabling Ubiquitous Visual Intelligence Through Deep Learning," by Dr. Ren Wu, formerly distinguished scientist at Baidu's Institute of Deep Learning (IDL). Dr. Wu shares an insider's perspective on the practical use of neural networks for vision. In "Navigating the Vision API Jungle: Which API Should You Use and Why?", Neil Trevett, President of the Khronos Group, maps the landscape of APIs for vision software development. Long-time Alliance collaborator Gary Bradski, President of the OpenCV Foundation, provides an insider's perspective on the new version of OpenCV and how vision developers can utilize it in his presentation, "The OpenCV Open Source Computer Vision Library: Latest Developments." Also make sure to take a look at "Harman's Augmented Navigation Platform: The Convergence of ADAS and Navigation" from that company's Vice President of Technology Strategy, Alon Atsmon. Roberto Mijat, Visual Computing Marketing Manager at ARM, explores when it makes sense to utilize a graphics core as a coprocessor in his presentation, "Understanding the Role of Integrated GPUs in Vision Applications." And echoing Dr. Wu's neural network focus, Jeff Gehlhaar, Vice President of Technology at Qualcomm, used his presentation "Deep-learning-based Visual Perception in Mobile and Embedded Devices: Opportunities and Challenges" to discuss the benefits, challenges and solutions for implementing neural networks in mobile and embedded devices. And the insights continued the next day at the quarterly Alliance Member Meeting: in "Combining Vision, Machine Learning and Natural Language Processing to Answer Everyday Questions," Faris Alqadah, CEO and Co-Founder of QM Scientific, explains how his firm is simplifying shopping for consumers by extracting and organizing product information from many data sources. While you're on the Alliance website, make sure to check out all the other great content recently published there. And for timely notification of the publication of new content, subscribe to our RSS feed and Facebook, Google+, LinkedIn company and group, and Twitter social media channels. Thanks as always for your support of the Embedded Vision Alliance, and for your interest in and contributions to embedded vision technologies, products and applications. Please don't hesitate to let me know how the Alliance can better serve your needs. Brian Dipert Editor-In-Chief, Embedded Vision Alliance

FEATURED VIDEOS

"Embedded Lucas-Kanade Tracking: How It Works, How to Implement It, and How to Use It," a Presentation from Goksel Dedeoglu of Perceptonic Goksel Dedeoglu, Ph.D., Founder and Lab Director of PercepTonic, presents the "Embedded Lucas-Kanade Tracking: How It Works, How to Implement It, and How to Use It" tutorial at the May 2014 Embedded Vision Summit. This tutorial is intended for technical audiences interested in learning about the Lucas-Kanade (LK) tracker, also known as the Kanade-Lucas-Tomasi (KLT) tracker. Invented in the early 80s, this method has been widely used to estimate pixel motion between two consecutive frames. Dedeoglu presents how the LK tracker works and discuss its advantages, limitations, and how to make it more robust and useful. Using DSP-optimized functions from TI's Vision Library (VLIB), he also shows how to detect feature points in real-time and track them from one frame to the next using the LK algorithm. He demonstrates this on Texas Instruments' C6678 Keystone DSP, where he detects and tracks thousands of Harris corner features in 1080p HD resolution video. Introduction to the Embedded Vision Opportunity and the Embedded Vision Alliance Community Jeff Bier, Founder of the Embedded Vision Alliance and President and Co-Founder of BDTI, presents the introductory remarks at the May 2015 Embedded Vision Summit. Jeff provides an overview of the embedded vision market opportunity, challenges, solutions and trends. He also introduces the Embedded Vision Alliance and the resources it offers for both product creators and potential members, and reviews the event agenda and other logistics. More Videos

FEATURED ARTICLES

Neural Network Processors: Has Their Time Come? Lately, neural network algorithms have been gaining prominence in computer vision and other fields where there's a need to extract insights based on ambiguous data. Classical computer vision algorithms typically attempt to identify objects by first detecting small features, then finding collections of these small features to identify larger features, and then reasoning about these larger features to deduce the presence and location of an object of interest (such as a face). These approaches can work well when the objects of interest are fairly uniform and the imaging conditions are favorable, but they often struggle when conditions are more challenging. An alternative approach, convolutional neural networks ("CNNs"), massively parallel algorithms made up of layers computation nodes, have been showing impressive results on these more challenging problems. More Facebook Oculus Acquires Pebbles Interfaces for Gesture Control Last month, Facebook's subsidiary Oculus reported that it had acquired Israel-based Pebbles Interfaces. Based in Israel, Pebbles Interfaces has spent the past five years developing technology that uses custom optics, sensor systems and algorithms to detect and track hand movement. Pebbles Interfaces will be joining the hardware engineering and computer vision teams at Oculus to help advance virtual reality, tracking, and human-computer interactions. More More Articles

FEATURED COMMUNITY DISCUSSION

Faculty Position Open at UC Santa Cruz More Community Discussions

FEATURED NEWS

Upcoming Free Qualcomm Vuforia Webinar Discusses Enabling Mobile Apps to See Intel Expands Developer Opportunities as Computing Expands Across All Areas of Peoples' Lives Altera Launches Worldwide SoC FPGA Developers Forums ON Semiconductor Introduces Series of Advanced Image Co-Processors for Next Generation Automotive Camera More News

About This E-Mail Embedded Vision Insights respects your time and privacy. If you are not interested in receiving future newsletters on this subject, please reply to this message with the word "UNSUBSCRIBE" in the Subject area. LETTERS AND COMMENTS TO THE EDITOR: Letters and comments can be directed to the editor, Brian Dipert, at editor@embeddedvisioninsights.com. PASS IT ON...Feel free to forward this newsletter to your colleagues. If this newsletter was forwarded to you and you would like to receive it regularly, click here to register.

 

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Fwd: Invitation: Let's build a humanoid robot with computer vision

---------- Forwarded message ----------
From: Hack A Robot <info@meetup.com>
Date: Saturday, August 22, 2015
Subject: Invitation: Let's build a humanoid robot with computer vision
To: john.sokol@gmail.com

New Meetup

Let's build a humanoid robot with computer vision

Hack A Robot

Added by Thomas Lee

Thursday, August 27, 2015
6:30 PM

South bay area
TBD
South bay area, CA

Are you going? Yes No

(Venue of this event is TBD.  Looking for suggestions/ offers to host this event as well) Earlier this year, I created Hackabot Nano (a feature-rich Arduino compatible wheeled robot). It was crowdfunded through Kickstarter. The robot was p...
Learn more

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Taking Atlas For a Walk

Remember Atlas, the humanoid robot from Boston Dynamics? The company bought by Google, er, owned by Alphabet, and uh, most likely to become Skynet? Well -- they've just shown us that Atlas can take a ...

http://hackaday.com/2015/08/17/taking-atlas-for-a-walk/

Driving an RC Car with Arduino and a USB Racing Wheel | Make: DIY Projects, How-Tos, Electronics, Crafts and Ideas for Makers

http://makezine.com/2015/08/10/driving-an-rc-car-with-arduino-and-a-usb-racing-wheel/

Lynxmotion - Tracked Vehicles

http://www.lynxmotion.com/c-109-tracked-vehicles.aspx


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