• Corpus ID: 9386607

A Keygraph Classification Framework for Real-Time Object Detection

  title={A Keygraph Classification Framework for Real-Time Object Detection},
  author={Marcelo Hashimoto and Roberto M. Cesar},
In this paper, we propose a new approach for keypoint-based object detection. Traditional keypoint-based methods consist in classifying individual points and using pose estimation to discard misclassifications. Since a single point carries no relational features, such methods inherently restrict the usage of structural information to the pose estimation phase. Therefore, the classifier considers purely appearance-based feature vectors, thus requiring computationally expensive feature extraction… 


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  • 1986
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  • U. S. Patent
  • 1962
net: Open Computer Vision Library. http://sourceforge.net/projects/opencvlibrary
  • net: Open Computer Vision Library. http://sourceforge.net/projects/opencvlibrary