Under controlled conditions, simple objects can be recognized using just elementary texture and color features. For example, we can recognize playing cards from histograms of local binary patterns and hue values.
First we must locate the cards in the scene. To do this we can use any method which is able to find rectangles with the right aspect ratio:
Then we rectify the perspective views:
And finally the feature vector which characterizes each card is computed from the rectified view. Classification is based on the nearest neighbor method:
(This is just an example used to test some camera combinators in the library. Cards can be better recognized by many other methods.)
In fact, LBP histograms can be directly used to characterize more complex, non planar objects. There is a simple illustrative easyVision program (simpleclassifier.hs) for recognition of objects in a small catalog. New prototypes can be added with a mouse click...