Linear Operator for Object Recognition
Abstract
Visual object recognition involves the identification of images of 3-D ob(cid:173) jects seen from arbitrary viewpoints. We suggest an approach to object recognition in which a view is represented as a collection of points given by their location in the image. An object is modeled by a set of 2-D views together with the correspondence between the views. We show that any novel view of the object can be expressed as a linear combination of the stored views. Consequently, we build a linear operator that distinguishes between views of a specific object and views of other objects. This opera(cid:173) tor can be implemented using neural network architectures with relatively simple structures.
Cite
Text
Basri and Ullman. "Linear Operator for Object Recognition." Neural Information Processing Systems, 1991.Markdown
[Basri and Ullman. "Linear Operator for Object Recognition." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/basri1991neurips-linear/)BibTeX
@inproceedings{basri1991neurips-linear,
title = {{Linear Operator for Object Recognition}},
author = {Basri, Ronen and Ullman, Shimon},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {452-459},
url = {https://mlanthology.org/neurips/1991/basri1991neurips-linear/}
}