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Text replacement - " Augmented reality " to " augmented reality "
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−{{stub}}+[[Object recognition]], as part of [[computer vision]], is an important feature in both [[Augmented reality]] and [[virtual reality]]. [[AR]] uses object recognition to deliver contextually aware information and multimedia. In [[VR]], many systems such as [[Chaperone]] of [[SteamVR]] are designed in such a way that they are aware of their surroundings, which is essential for safe operation. As VR and AR technologies become more mainstream, we will see a dramatic improvement in object recognition. These improvements will have to overcome, among many other things, varying lighting conditions, changes in shape and size, and work fast enough to be usable in everyday situations.
===Google Glass===+[[Google Glass]] was the first system to demonstrate how object recognition can be used in AR. First released by [[Google]] in February 2013, the system uses a special liquid crystal on silicon (LCoS), [[optical head-mounted display]] with 640x360 pixels [[resolution]] to display information from many existing Google applications, as well as those created by third-party developers. Google Glass is able to recognize an image in a scene, [[positional tracking|track its position]], and augment the display by displaying appropriate information. This may be a map with navigation, price information for shoppers, office and business hours, or nutrition information of food items.
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−Development of augmented reality apps for Google Glass is made very accessible thanks to several SDKs that are currently available with [[Wikitude]] SDK being the most popular. The SDK enables developers to take advantage of built-in image recognition and tracking technology, position-aware services with geo-referenced data, embed videos from [[YouTube]] or Vimeo, and more. It allows anybody who is interested in augmented reality and object recognition to quickly develop a new application and showcase just how this technology could be integrated with our daily lives. Other solutions include, for example, [[CraftAR]], which is a suite of tools created for agencies, publishers, and companies that would like to leverage technological possibilities of AR and IR to create ads, magazines, and catalogs. The technology works on all major mobile operating systems and supports the most popular programming languages and 3D engines.
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−==Virtual Reality==
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'''Object recognition''', as part of [[computer vision]], is an important feature in both [[augmented reality]] and [[virtual reality]]. [[AR]] uses object recognition to deliver contextually aware information and multimedia. In [[VR]], many systems such as [[Chaperone]] of [[SteamVR]] are designed in such a way that they are aware of their surroundings, which is essential for safe operation. As VR and AR technologies become more mainstream, we will see a dramatic improvement in object recognition. These improvements will have to overcome, among many other things, varying lighting conditions, changes in shape and size, and work fast enough to be usable in everyday situations.
−==How does object recognition work?==
==How does object recognition work?==
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Feature-based methods look for similar features in an imagined or ideal object and a real image. When we consider, for example, a face recognition, it is possible to program a set of features that are associated with the human face. Using these features, a software algorithm can generate a model that will be placed over the captured image. If some features of this object match the image we have a positive match. Common feature-based detection methods include interpretation trees, hypothesize and test method, pose consistency, pose clustering, invariance, geometric hashing, scale-invariant feature transform method, and speeded up robust features (SURF).
Feature-based methods look for similar features in an imagined or ideal object and a real image. When we consider, for example, a face recognition, it is possible to program a set of features that are associated with the human face. Using these features, a software algorithm can generate a model that will be placed over the captured image. If some features of this object match the image we have a positive match. Common feature-based detection methods include interpretation trees, hypothesize and test method, pose consistency, pose clustering, invariance, geometric hashing, scale-invariant feature transform method, and speeded up robust features (SURF).
−==Augmented Reality==
+==References==
−{{Reflist}}
−[[Category:Terms]]
[[Category:Terms]]