PROMISE Winter School 2012 - Information Retrieval meets Information Visualization

Zinal, Valais - Switzerland
23 - 27 January 2012

The aim of the Promise Winter School on information retrieval and information visualization is to give participants a grounding in the core topics that constitute the multidisciplinary area of Multilingual Information Retrieval. The school is a week-long event consisting of guest lectures from invited speakers who are recognized experts in the field. The School is intended for  PhD students, Masters students or senior researchers such as post-doctoral researchers form the fields of information visualization and information retrieval and related fields.

General Chair

• Tiziana Catarci, Sapienza, University of Rome, Italy

Program Commitee

• Maristella Agosti, University of Padua, Italy
• Nicola Ferro, University of Padua, Italy
• Henning Müller, University of Applied Sciences Western Switzerland, Switzerland
• Guiseppe Santucci, Sapienza, University of Rome, Italy

Publicity Chair

• Pamela Forner, Centre for the Evaluation of Language and Communication Technologies (CELCT), Italy
• Hélène Mazo, Evaluations and Language resources Distribution Agency (ELDA), France

Local Organization

• Alexandre Cotting, University of Applied Sciences Western Switzerland, Switzerland
• Henning Müller, University of Applied Sciences Western Switzerland, Switzerland

Contacts

• For any information please contact  winter-school[at] promise-noe.eu

 

http://www.promise-noe.eu/events/winter-school-2012/

SiftGPU: A GPU Implementation of Scale Invariant Feature Transform (SIFT)

http://www.cs.unc.edu/~ccwu/siftgpu/ - Changchang Wu

SIFT Implementation

SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors.

SiftGPU is inspired by Andrea Vedaldi's sift++[2] and Sudipta N Sinha et al's GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified.

SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided.

Requirements

SiftGPU requires a high-end GPU (like nVidia 8800) that has a large graphic memory and supports dynamic branching. GLSL for OpenGL is used by default, and CUDA is provided as an alternative for nVidia graphic cards. Haven't fully tested on ATI, but the GLSL shaders did pass the AMD Shader Analyzer (Catalyst 8.12), it should be working.

SiftGPU uses GLEW 1.51, DevIL1.77 (can be disabled), GLUT(viewer only), and CUDA(optional). You'll need to make sure that your system has all the dependening libraries of corresponding versions. To update the libaries, you'll need to replace the header files in SiftGPU\Include\, and the corresponding binaries.

NOTE FOR CUDA : 1. The thread block setting is currently tuned on nVidia GTX 8800. It may not be optimized for other GPUs. 2. The CUDA version is not compiled by default. You need to define CUDA_SIFTGPU_ENABLED to the compiler and recompile the package. For VS2010 users, you can just use SiftGPU_CUDA_Enabled solution.

Download

SiftGPU-V371 (5.0MB; Including code, manual , windows binary and some test images) Want to cite SiftGPU?
You might be interested in the Matlab Versions mex'd by Adam Chapman and by Parag. K. Mital

SimpleSIFT.cpp gives some examples of using SiftGPU and SiftMatchGPU.
Previous versions of SiftGPU can be found through this link. A complete change list can be found here.

Some minor updates since V360
  1. Converted the MSVC Solution to Visual Studio V2010 and tested CUDA4 (6/2011)
  2. Automatic switching from OpenGL to CUDA when OpenGL is not supported (1/2011)
  3. Dropped the indirect data transfer path CPU->GL->CUDA (1/2011)
  4. Dropped the CG implementation to simplify maintance (1/2011)
Some previous changes
  6. Added device selection for Multi-threading (Check the example at MultiThreadSIFT.cpp).
  5. Used SSE to speedup the descriptor normalization step for the OpenGL implementation.
  4. Added CUDA-based SiftGPU/SiftMatchGPU implementation. See Figure below for the speed.
  3. Added OpenGL-based sift matching implementation, check example #7 in manual. (Thanks to Zach)
  2. Added function to compute descriptors for user-specified keypoints, check example #6 in manual.
  1. Improved speed by %50 compared with V293. Look here for experiment details and explanations

Evaluation

Below is the evaluation of the speed of V340 on different image sizes. "-fo -1" means using upsampled image. "-glsl" uses GLSL and "-cuda" uses CUDA (The experiment images are all resized from this image) .

   System : nVidia 8800GTX, 768MB, Driver 182.08, Windows XP, Intel 3G P4 CPU, 3.5G RAM. (V311 Speed)

Below is the comparision with Lowe's SIFT on box.pgm using the comparision code from Vedaldi's SIFT .

References

[1]   D. G. Lowe. Distinctive image features from scale-invariant keypoints . International Journal of Computer Vision, November 2004.
[2]   A. Vedaldi. sift++, http://vision.ucla.edu/~vedaldi/code/siftpp/siftpp.html.
[3]   G. Ziegler, et al. GPU point list generation through histogram pyramids. In Technical Report, June 2006.
[4]   Sudipta N Sinha, Jan-Michael Frahm, Marc Pollefeys and Yakup Genc, "GPU-Based Video Feature Tracking and Matching ",
        EDGE 2006, workshop on Edge Computing Using New Commodity Architectures, Chapel Hill, May 2006

4th International Conference on Agents and Artificial Intelligence

ICAART (4th International Conference on Agents and Artificial Intelligence - http://www.icaart.org/) has an open call for papers, whose deadline is set for July 28, 2011. We hope you can participate in this conference by submitting a paper reflecting your current research in any of the following areas:

- Artificial Intelligence

- Agents

The conference will be sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and held in cooperation with the Portuguese Association for Artificial Intelligence (APPIA), the Spanish Association for Artificial Intelligence (AEPIA) and the Association for the Advancement of Artificial Intelligence (AAAI). INSTICC is member of the Foundation for Intelligent Physical Agents (FIPA), Workflow Management Coalition (WfMC) and the Object Management Group (OMG).

ICAART would like to become a major point of contact between researchers, engineers and practitioners interested in the theory and applications in these areas. BBCQ Informatics applications are pervasive in many areas of Artificial Intelligence and Distributed AI, including Agents and Multi-Agent Systems. This conference intends to emphasize this connection, therefore, authors are invited to highlight the benefits of Information Technology (IT) in these areas. Ideas on how to solve problems using agents and artificial intelligence, both in R&D and industrial applications, are welcome. Papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged.

The conference program features a number of Keynote Lectures to be delivered by distinguished world-class researchers, including those listed below.

All accepted papers (full, short and posters) will be published in the conference proceedings, under an ISBN reference, on paper and on CD-ROM support.

A short list of presented papers will be selected so that revised and extended versions of these papers will be published by Springer-Verlag in a CCIS Series book.

A short list of papers will be selected for publication in a special issue of JOPHA - Journal of Physical Agents.

The proceedings will be submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP and Elsevier Index (EI).

Best paper awards will be distributed during the conference closing session. Please check the website for further information (http://www.icaart.org/best_paper_awards.asp).

All papers presented at the conference venue will be available at the SciTePress Digital Library (http://www.scitepress.org/DigitalLibrary/). SciTePress is member of CrossRef (http://www.crossref.org/).

We also would like to highlight the possibility to submit to the following Satellite Workshop:

- 2nd International Workshop on Semantic Interoperability (IWSI)

Workshops, Special sessions as well as tutorials dedicated to other technical/scientific topics are also envisaged: companies interested in presenting their products/methodologies or researchers interested in holding a tutorial are invited to contact the conference secretariat. Workshop chairs and Special Session chairs will benefit from logistics support and other types of support, including secretariat and financial support, to facilitate the development of a valid idea.

Please check further details at the ICAART conference website (http://www.icaart.org).

Should you have any question please don't hesitate contacting me.

ICAART 2012 will be held in conjunction with ICPRAM (http://www.icpram.org/) and ICORES 2012 (http://www.icores.org/) in Vilamoura, Algarve, Portugal. Registration to ICAART will enable free access to the ICPRAM and ICORES conferences (as a non-speaker).

Card.io Lets You Pay on Mobile by Holding a Credit Card Up to the Phone

Card.io, a new San Francisco-based startup led by two former AdMob employees, Mike Mettler and Josh Bleecher, is introducing a revolutionary idea that could transform mobile commerce: make it easier to pay. But how the company has accomplished this is a feat that will feel more like magic to the everyday mobile user. With card.io, you simply hold your credit card up to the phone. The software then "sees" the card information using the phone's camera and the payment is processed. No typing required!

To get the technology into the hands of those who need it most, card.io is targeting iOS developers at launch, specifically those in the e-commerce, local, ticketing, travel and daily deals space.

Card Scanning is Very Accurate, Says Company

The company has been in existence for only nine months, six of which were spent building card.io'score technology, the computer vision and machine learning algorithms which it uses to read the numbers of a credit card. Unlike several other check-scanning and business card scanning software programs, card.io doesn't use humans to verify the accuracy of scans - everything is programmatic. Of course this means that the scans themselves have to be highly accurate, and Mettler says they are.

However, the company wouldn't provide exact percentages here, only that the "vast majority" of scans should be accurate. But that's where the machine learning aspect comes into play. The more data that's processed, the better the software performs because it learns and improves over time.

Once the credit card information is entered, the developer can then continue to process the payment using their own merchant account as usual. Although card.io will be looking for merchant partners going forward, that's not the service it's offering today. Also of note, all the data card.io handles is secured using 128 bit-SSL and the service never stores card images on the phone or on the company's servers.

 

Going After E-Commerce, Not Point-of-Sale

Using technology to input the credit card details into a mobile device may seem like a step backwards when positioned alongside other upcoming mobile advances like NFC, a wireless technology that lets you pay for real-world goods with just a tap.

But NFC requires a special chip in handsets, and currently, few phones on the market offer this. It's still years out from mainstream adoption. Meanwhile, everyone has credit cards and these plastic, physical cards won't disappear anytime soon. Most importantly, card.io is not trying to compete with NFC or other innovations at the point-of-sale - it's going after the e-commerce market.

With a new software development kit (SDK) available now, the iOS (iPhone, iPad and iPod Touch) developers accepted into the company's private beta can integrate the technology into their own app. Initially, as noted above, card.io is most interested in developers working in the e-commerce, local, ticketing, travel and daily deals space. It is already working with a few companies here, including MogoTix for event tickets, TaskRabbit for local services and SamaSource for donations.

Card.io received $1 million in seed funding in January, led by former eBay exec Michael Dearing of Harrison Metal. Other investors include Jeff Clavier and Charles Hudson of SoftTech VC, Manu Kumar of K9 Ventures, Alok Bhanot (former VP, Risk Technology at PayPal), and Omar Hamoui (CEO/founder of AdMob).

iOS developers interested in joining the private beta have to plead their case here.

http://www.readwriteweb.com/mobile/2011/06/cardio-lets-you-pay-on-mobile-by-holding-credit-card-up-to-phone.php

DeepShot: Computer vision app for computer-smartphone communication

This post by Computer Vision Central has been reprinted from Computer Vision Central's blog

Google Research and MIT CSAIL have developed a method for transferring an application's work state between a computer and a mobile phone using computer vision. The user can take an image of the computer screen using the mobile phone camera; the Deep Shotapp on the phone then identifies the current webpage on the computer and communicates with the computer to determine the URI of the application. For many web-based applications, the URI specifies both the application and the application state. Deep Shot also supports transfer in the other direction: an image of the computer display taken with the mobile phone is used to identify the computer to the phone. Deep Shot requires installation of software on both the computer and the phone. More information is available on Popular Science.

7th INTERNATIONAL SUMMER SCHOOL ON PATTERN RECOGNITION (ISSPR)

4-9 SEPTEMBER 2011, Plymouth, UK

http://pro.expressemail.in/link.php?M=1048308&N=3119&L=707&F=T

Registration deadline: 11th July, 2011

It is a pleasure to announce the Call for Participation to the 7th International Summer School on Pattern Recognition. I write to invite you, your colleagues, and students within your department to attend this event.

In 2010, the 6th ISSPR School held at Plymouth was a major success with over 90 participants. The major focus of 2011 summer school includes:

- A broad coverage of pattern recognition areas which will be taught in a tutorial style over five days by leading experts. The areas covered include statistical pattern recognition, Bayesian techniques, non-parametric and neural network approaches including Kernel methods, String matching, Evolutionary computation, Classifiers, Decision trees, Feature selection and Dimensionality reduction, Clustering, Reinforcement learning, and Markov models. For more details visit the event website.

- A number of prizes sponsored by Microsoft and Springer for best research demonstrated by participants and judged by a panel of experts. The prizes will be presented to the winners by Prof. Chris Bishop from Microsoft Research.

- Providing participants with knowledge and recommendations on how to develop and use pattern recognition tools for a broad range of applications.

10 Corporate Scholarships are available towards discounted registration fee for students till 11th July, 2011 so this is an excellent opportunity for participants to register at an affordable cost. The fee includes registration and accommodation plus meals at the event. The registration process is online through the school website www.patternrecognitionschool.com which has further details on registration fees. Please note that the number of participants registering each year at the summer school is high with a limited number of seats available, and therefore early registration is highly recommended.