TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations
Abstract
We describe a model that can recognize two-dimensional shapes in an unsegmented image, independent of their orientation, position, and scale. The model, called TRAFFIC, efficiently represents the structural relation between an object and each of its component features by encoding the fixed viewpoint-invariant transformation from the feature's reference frame to the object's in the weights of a connectionist network. Using a hierarchy of such transformations, with increasing complexity of features at each successive layer, the network can recognize multiple objects in parallel. An implemen(cid:173) tation of TRAFFIC is described, along with experimental results demonstrating the network's ability to recognize constellations of stars in a viewpoint-invariant manner.
Cite
Text
Zemel et al. "TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations." Neural Information Processing Systems, 1989.Markdown
[Zemel et al. "TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations." Neural Information Processing Systems, 1989.](https://mlanthology.org/neurips/1989/zemel1989neurips-traffic/)BibTeX
@inproceedings{zemel1989neurips-traffic,
title = {{TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations}},
author = {Zemel, Richard S. and Mozer, Michael and Hinton, Geoffrey E.},
booktitle = {Neural Information Processing Systems},
year = {1989},
pages = {266-273},
url = {https://mlanthology.org/neurips/1989/zemel1989neurips-traffic/}
}