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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.

'''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?==

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==

 

[[Category:Terms]]

[[Category:Terms]]