Scale and rotation invariant color features for weakly-supervised object Learning in 3D space

@article{Kanezaki2011ScaleAR,
  title={Scale and rotation invariant color features for weakly-supervised object Learning in 3D space},
  author={Asako Kanezaki and Tatsuya Harada and Yasuo Kuniyoshi},
  journal={2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)},
  year={2011},
  pages={617-624}
}
We propose a joint learning method for object classification and localization using 3D color texture features and geometry-based segmentation from weakly-labeled 3D color datasets. Recently, new consumer cameras such as Microsoft's Kinect produce not only color images but also depth images. These reduce the difficulty of object detection dramatically for the following reasons: (a) reasonable candidates for object segments can be given by detecting spatial discontinuity, and (b) 3D features that… CONTINUE READING
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  • A. Kanezaki, T. Suzuki, T. Harada, Y. Kuniyoshi
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