Object detection using a single almost identical reference image

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I would like to understand what solutions there are to perform object detection using a single almost identical reference image on a picture or in an augmented reality setting.

To be more specific: I want to detect flat (i.e. 2-dimensional) and mostly rectangular objects. I have a database with "perfect" reference images (high quality, full frontal, exact colors, no alterations, etc.) of the objects to be detected but I may have only one reference for each object. I am talking about things such as logos, famous paintings and playing cards so the reference will have exactly the same content, shape and proportions as the object. From my understanding, the only difference between the object and the reference could then be perspective and a difference in lighting conditions. Let's assume none of these are very extreme (e.g. no sharp angle or colored light).

I know that image recognition and object detection usually requires many training images but given these simplified conditions, is there a way to make it work with one or few images (or create several by transforming one)?

I looked here and elsewhere and the only thing I found so far was this example of the Vuforia SDK: https://www.youtube.com/watch?v=MtiUx_szKbI&t=1m10s. One image of a card in a card game is apparently enough to create an overlay so I assume there are ways. This is not my field of expertise so I hope you guys can help me out :)

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computer-visionaugmented-realityobject-detectionimage-recognition

1 Answers

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If there were no perspective distortion, you could use simple normalized cross-correlation. But since there is, you probably want to use SURF. The basic algorithm to use SURF to find your reference image within a world image is:

  • find keypoints, such as corners, in both images.
  • describe the local texture of each keypoint.
  • use those descriptors to match keypoints between images. If there are a lot of matches, with consistent geometry, you've probably found your object.

Check out this tutorial, that walks you through doing exactly that: http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html#feature-homography