Extract from  http://www.vox.com/2014/11/21/7259207/scientific-paper-scam:

The paper titled “Get me off your fucking mailing list” has been accepted by the International Journal of Advanced Computer Technology. 

Let us explain. 

The journal, despite its distinguished name, is a predatory open-access journal, as noted by io9. These sorts of low-quality journals spam thousands of scientists, offering to publish their work for a fee. 

In 2005, computer scientists David Mazières and Eddie Kohler created this highly profane ten-page paper as a joke, to send in replying to unwanted conference invitations. It literally just contains that seven-word phrase over and over, along with…

North – Dominion Plaza,
17304,Preston Road,
Suite 800, Dallas, TX 75252 
Tel: +1-888-600-6441
Email: sales@marketsandmarkets.com

The report “Image Recognition Market [Method (OCR, Object, Pattern Matching, Barcode/ QR, Facial), Application (Security & Surveillance, Quality Control & Inventory Management, Advertising, Scanning & Imaging, m-Commerce)] – Worldwide Market Forecast (2014-2019)” defines and divides the image recognition market into various subsegments along with a detailed analysis and forecasting of segment-wise revenues. It also explains the drivers and restraints for this market and identifies the opportunities, challenges, and ongoing trends.

http://www.marketsandmarkets.com/Market-Reports/image-recognition-market-222404611.html

Today, the image recognition solutions are not only fulfilling basic human requirements, such as purchasing numerous products through smartphone-based applications, but are also providing state-of-the-art technology facilitated equipment for sensitive layouts such as defense, airports, immigration, and customs departments. Also, the Intelligent Character Recognition (ICR) technology is used in sorting and enumerating important files for organizational purposes. It helps in accessing a particular document with ease and saving time.MarketsandMarkets has comprehensively divided the image recognition market by types: Optical Character Recognition (OCR), pattern and gradient matching, object recognition, barcode/QR code recognition, facial recognition; by components: hardware, software, and services; by deployment: on-premise and on-demand; by applications: security and surveillance, marketing and promotional activities, content management, advanced driving assistance, quality control and inventory management, scanning and imaging, m-commerce, and others; by verticals: government and defense, Banking, Financial services, and Insurance (BFSI), healthcare and life sciences, manufacturing/industrial units, consumer goods and retail, IT and telecommunication, transport and logistics and others; by regions: North America (NA), Europe (EU), Asia-Pacific (APAC), Latin America (LA) and Middle East and Africa (MEA).MarketsandMarkets believes that there is a lack of user awareness and a few technological quality issues in the image recognition market. These challenges are restraining businesses and customers from implementing image recognition technology to support a broad spectrum of applications, such as security and surveillance, marketing and promotional activities, content management, advanced driving assistance, quality control and inventory management, scanning and imaging, and m-commerce.The image recognition market is estimated to grow from $9.65 billion in 2014 to $25.65 billion by 2019 at a Compound Annual Growth Rate (CAGR) of 21.6% from 2014 to 2019. NA is the biggest market for image recognition solutions while other regions such as APAC and MEA are expected to increase with high CAGRs.

Description

Video demo

 

Database

https://sites.google.com/site/visualgestrecog/
http://www.gti.ssr.upm.es/data/HandGesture_database.html

This is a very high quality (and hard) online (and free) course about Probabilistic Graphical Models developed in the MOOC Coursera platform. The course is given by Daphne Koller from the Stanford University. This course can be the difference between a standard thesis and a brilliant one.

The book associated with this course can be view here [Link].

Interesting video from TED.com:

• Matlab interface for VS-PETS 2003 [Link 1] [Link 2] (Updated 13 July 2012)
• Contains classes for handling trajectories and detections.

Courses 2.0 (opencourseware, MOOC)

• Coursera [Link].
• Online courses from several world-class universities.
• Interesting courses in the area of computer vision:
• Machine Learning.Stanford University. Andrew Ng, Associate Professor.
• Computer Vision: The Fundamentals. University of California, Berkeley. Jitendra Malik.
• MIT OpenCourseWare [Link]
• Online courses from MIT.
• Udacity [Link]
• Online courses founded by four Stanford roboticists.
• edX [Link]
• Online courses from MIT and Harvard university.
• It is a new and promising project that has not started yet.
• Khan Academy [Link]
• Open culture [Link]
• Online courses, audiobooks, movies, languages lessons, and ebooks from Ivy League universities, such as Stanford, UCLA, Columbia and Oxford University.
• TED-ED [Link]
• From TED.com.
• Memrise [Link]
• Language lessons and other courses.

From Wikipedia:

References

1. Martin A. Fischler and Robert C. Bolles (June 1981). “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”. Comm. of the ACM 24 (6): 381–395. doi:10.1145/358669.358692.
2. David A. Forsyth and Jean Ponce (2003). Computer Vision, a modern approach. Prentice Hall. ISBN 0-13-085198-1.
3. Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in Computer Vision (2nd ed.). Cambridge University Press.
4. P.H.S. Torr and D.W. Murray (1997). “The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix”. International Journal of Computer Vision 24 (3): 271–300. doi:10.1023/A:1007927408552.
5. Ondrej Chum (2005). “Two-View Geometry Estimation by Random Sample and Consensus”. PhD Thesis
6. Sunglok Choi, Taemin Kim, and Wonpil Yu (2009). “Performance Evaluation of RANSAC Family”. In Proceedings of the British Machine Vision Conference (BMVC).

External links

1. RANSAC Toolbox for MATLAB. A research (and didactic) oriented toolbox to explore the RANSAC algorithm in MATLAB. It is highly configurable and contains the routines to solve a few relevant estimation problems.
2. Implementation in C++ as a generic template.
3. RANSAC for Dummies A simple tutorial with many examples that uses the RANSAC Toolbox for MATLAB.
4. 25 Years of RANSAC Workshop
5. Source code for RANSAC in MATLAB
6. Ransac.js Javascript implementation with visual representation of the iterations (Example of 2D Line fitting).