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Depth-from-Recognition: Inferring Metadata by Cognitive Feedback

A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, L. Van Gool
ICCV'07 Workshop on 3D Representations for Recognition
Rio de Janeiro, Brasil, October 2007

Abstract

Thanks to recent progress in category-level object recognition, we have now come to a point where these techniques have gained sufficient maturity and accuracy to succesfully feed back their output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer meta-data such as depth cues, 3D points, or object decomposition in images of previously unseen object instances. Our approach builds on the Implicit Shape Model of Leibe and Schiele, and extends it to transfer annotations from training images to test images. Experimental results validate the viability of our approach.


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@InProceedings{eth_biwi_00500,
  author = {A. Thomas and V. Ferrari and B. Leibe and T. Tuytelaars and L. Van Gool},
  title = {Depth-from-Recognition: Inferring Metadata by Cognitive Feedback},
  booktitle = {ICCV'07 Workshop on 3D Representations for Recognition},
  year = {2007},
  month = {October},
  keywords = {}
}