A.L. Majdik, D. Verda, Y. Albers-Schoenberg, D. Scaramuzza, Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles, Journal of Field Robotics, 2015.
Read the paper http://rpg.ifi.uzh.ch/docs/JFR15_Majdik.pdf
Friday, May 22, 2015
Dyson 360 Eye and Baidu Deep Learning at the Embedded Vision Summit in Santa Clara
Article From http://www.computervisionblog.com/2015/05/dyson-360-eye-and-baidu-deep-learning.html
While vision has been a research priority for decades, the results have often remained out of reach of the consumer. Huge strides have been made, but the final, and perhaps toughest, hurdle is how to integrate vision into real world products. It’s a long road from concept to finished machine, and to succeed, companies need clear objectives, a robust test plan, and the ability to adapt when those fail.
Image from ExtremeTech: Dyson 360 Eye: Dyson’s ‘truly intelligent’ robotic vacuum cleaner is finally here
The Dyson 360 Eye robot vacuum cleaner uses computer vision as its primary localization technology. 10 years in the making, it was taken from bleeding edge academic research to a robust, reliable and manufacturable solution by Mike Aldred and his team at Dyson.
Mike Aldred’s keynote at next week's Embedded Vision Summit (May 12th in Santa Clara) will chart some of the high and lows of the project, the challenges of bridging between academia and business, and how to use a diverse team to take an idea from the lab into real homes.
Enabling Ubiquitous Visual Intelligence Through Deep Learning
Ren Wu
Distinguished Scientist, Baidu Institute of Deep Learning
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans. Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
Ren Wu’s morning keynote at next week's Embedded Vision Summit (May 12th in Santa Clara) will share an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his team at Baidu.
Read More http://www.computervisionblog.com/2015/05/dyson-360-eye-and-baidu-deep-learning.html
Love affair with CBIR
Article from Technical Insanity
In this article, I will walkthru the sample application, which I have created to demonstrate the working of CBIR. This is continuation of the series of article, so, if you are not able to catch with this post, read the previous ones to understand the context.
Last article, you have probably read about the theory involve in finding the similar image. But, you won’t get it, till you see the working code. Same was my frustration, when I was doing the research for the CBIR stuff. I find tons of papers from various universities around the world. But, it was hard to find working demo, especially in .NET I googled for days, found blogs, article, but most of it doesn’t share implementation code. Most of the Computer Vision stuff happens in C++\Matlab\Phyton. Very few taker for .NET languages.
That’s where I decided to help the poor souls like me, who are searching for CBIR implementation in .NET would get benefited with this proof of concept application. Especially, college student’s who are trying to learn C# language, .NET framework and trying to built CBIR all at same time. This would be boon from them, like the oasis in the desert of internet
I would be showing you demo on the Wang image dataset. You can download 1000 test images, which contain 10 sets of 100 images each. This will help you understand how reverse image search works, and how efficient the algorithm is, in getting the similar image.
You can download Image Database application from https://github.com/sbrakl/ImageDatabase
License
Note: This application which I have build is under GPLv3 license, but the libraries it (EMGU CV, Accord Framework, etc.) aren’t under same license. If you need to use it in commercial application, I request you to check the license of individual libraries used in this application, and use it appropriately.
This utility is been developed in WPF, .NET framework 4.5 as the proof of concept for various image recognition algorithm.
This utility consists of three areas
Wednesday, April 29, 2015
ICRA 2015 AVERT: An Autonomous Multi-Robot System for Vehicle Extraction and Transportation
Accompanying video for the paper "AVERT: An Autonomous Multi-Robot System for Vehicle Extraction and Transportation" to be presented at the International Conference on Robotics and Automation (ICRA2015), May 26-30, Seattle, Washington, USA.
Authors: Angelos Amanatiadis, Christopher Henschel, Bernd Birkicht, Benjamin Andel, Konstantinos Charalampous, Ioannis Kostavelis, Richard May, and Antonios Gasteratos.
In this video, a swarm of robots is able to extract vehicles from confined spaces with delicate handling, swiftly and in any direction. The novel lifting robots are capable of omnidirectional movement, thus they can under-ride the desired vehicle and dock to its wheels for a synchronized lifting and extraction. The overall developed system applies reasoning about available trajectory paths, wheel identification, local and undercarriage obstacle detection, in order to fully automate the process.
Included in the official ICRA 2015 Video Trailer https://youtu.be/OM_1F33fcWk?t=70
You can read a preprint of the paper here: http://robotics.pme.duth.gr/docs/ICRA...
Tuesday, March 24, 2015
European Summer School in Information Retrieval (ESSIR)
The European Summer School in Information Retrieval (ESSIR) is a scientific event founded in 1990, which has given rise to a series of Summer Schools held on a regular basis to provide high quality teaching of Information Retrieval (IR) and advanced IR topics to an audience of researchers and research students. ESSIR is typically a week-long event consisting of guest lectures and seminars from invited lecturers who are recognised experts in the field.
The 10th European Summer School in Information Retrieval (ESSIR 2015) will be held in Thessaloniki, Greece hosted by the Multimedia Knowledge and Social Media Analytics Laboratory (MKLab) of the Information Technologies Institute (ITI) at theCentre for Research and Technology Hellas (CERTH).
ESSIR 2015 will be a 5-day event (31 August – 4 September, 2015) that will offer a high quality teaching on IR and related research topics, in a friendly atmosphere. A new edition of the Symposium on Future Directions in Information Access (FDIA) will also be held at ESSIR 2015, which will provide a forum for early researchers to present their research in a friendly environment, whilst among senior researchers.
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