Featured Research

from universities, journals, and other organizations

Teaching Computers To Recognize Objects

Date:
June 2, 2009
Source:
ICT Results
Summary:
Recognizing objects and groups of objects is something we humans take for granted. For computers, this is far from straightforward. A European project has come up with novel solutions to this conundrum.

Recognising objects and groups of objects is something we humans take for granted. For computers, this is far from straightforward. A European project has come up with novel solutions to this conundrum.

Related Articles


Imagine your friends have blindfolded you and taken you to a “secret location”. When they take off your blindfold, you immediately see a group of people around you and realise that they have thrown you a surprise birthday party. How did you know? Because everyone shouted “surprise”, and there were balloons, a birthday cake and booze.

The question may seem like a silly one, but the processes involved are far from straightforward. In fact, you had to collate an awful lot of visual, as well as other sensory data, cross-reference it with your memories, and make mental deductions.

“Vision is our most important sense and about half of the human brain is involved with vision in one way or another,” explains Luc Van Gool of Belgium’s Leuven University (KUL) who also leads the Computer Vision Laboratory at the Swiss Federal Institute of Technology (ETH). “Enabling us to recognise the objects and places around us is a task it performs brilliantly.”

In fact, what we regard as the simple process of “recognition” would leave many computers stumped. Even something as apparently simple as recognising a birthday cake would normally require computers to be fed with information on what a cake generally looks like, the various shapes and sizes it comes in, the different forms and numbers of candles and other decorations you are likely to find adorning it, etc.

“The same object will look different depending on the viewpoint, the illumination, or the occlusions caused by other objects in front,” notes Van Gool.

Points of view

In brief, computers might be able to calculate pi to hundreds of decimal points and model complex weather patterns, but they may find it impossible, without complex and painstaking programming, to recognise a human whose grown their hair or realise that Chihuahuas and Dobermans belong to the same species.

Van Gool is involved in a project, Cognitive-Level Annotation Using Latent Statistical Structure (CLASS -- http://class.inrialpes.fr/), which is developing technologies to recognise visually specific objects, such as your car, or classes of object, such as a random car on the street.

“The recognition of an object as belonging to a particular group is a harder problem for a computer than the recognition of a specific object. The reason is that object classes show large variability among their members,” Van Gool points out.

The 3.5-year, EU-funded project managed to achieve technological improvements compared with previous efforts. It developed a system in which the description of the objects is based on the appearance of many separate, small patches. Such localised features give the necessary robustness to deal with the massive variations mentioned earlier. In addition, CLASS created special mechanisms – known as efficient approximate neighbourhood searches – for the comparison of an image or an object with huge numbers of reference images.

A picture speaks a thousand words

The specific object recognition technology developed by CLASS has already found a commercial application. Through a company known as kooaba, CLASS technology enables mobile phone subscribers who install the relevant software to take a photo with their handset of, say, a monument, a film poster, or an album cover and get relevant online information about it.

“It’s like the object itself becomes the link to further information,” observes Van Gool. He expects the application of this technology to expand rapidly. For instance, cities and museums may offer interactive guided tours or guide books through kooaba.


Story Source:

The above story is based on materials provided by ICT Results. Note: Materials may be edited for content and length.


Cite This Page:

ICT Results. "Teaching Computers To Recognize Objects." ScienceDaily. ScienceDaily, 2 June 2009. <www.sciencedaily.com/releases/2009/06/090601090029.htm>.
ICT Results. (2009, June 2). Teaching Computers To Recognize Objects. ScienceDaily. Retrieved February 28, 2015 from www.sciencedaily.com/releases/2009/06/090601090029.htm
ICT Results. "Teaching Computers To Recognize Objects." ScienceDaily. www.sciencedaily.com/releases/2009/06/090601090029.htm (accessed February 28, 2015).

Share This


More From ScienceDaily



More Computers & Math News

Saturday, February 28, 2015

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Could a $34 Smartphone Device Improve HIV Diagnosis in Africa?

Could a $34 Smartphone Device Improve HIV Diagnosis in Africa?

Reuters - Innovations Video Online (Feb. 27, 2015) A dongle that plugs into a Smartphone mimics a lab-based blood test for HIV and syphilis and can detect the diseases in 15 minutes, say researchers. Tara Cleary reports. Video provided by Reuters
Powered by NewsLook.com
Android's Popularity Doesn't Mean Profits For Google

Android's Popularity Doesn't Mean Profits For Google

Newsy (Feb. 26, 2015) Seventy percent of smartphones shipped last year were Android but that OS only accounted for 11 percent of total smartphone profits. Video provided by Newsy
Powered by NewsLook.com
Lenovo Hack May Be Retaliation For 'Superfish' Vulnerability

Lenovo Hack May Be Retaliation For 'Superfish' Vulnerability

Newsy (Feb. 26, 2015) Lenovo&apos;s website was hacked by what appears to be the infamous Lizard Squad group. The attack seems to be related to Lenovo&apos;s "Superfish" controversy. Video provided by Newsy
Powered by NewsLook.com
Google's Artificial Intelligence Can Dominate Atari Video Games

Google's Artificial Intelligence Can Dominate Atari Video Games

Buzz60 (Feb. 26, 2015) Google&apos;s artificial intelligence, DeepMind, has figured out how to play and master a handful of Atari video games. Brett Larson explains. Video provided by Buzz60
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:

Breaking News:

Strange & Offbeat Stories


Space & Time

Matter & Energy

Computers & Math

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile: iPhone Android Web
Follow: Facebook Twitter Google+
Subscribe: RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins