ML Anthology

Object Indexing Using an Iconic Sparse Distributed Memory

Rajesh P. N. Rao, Dana H. Ballard
ICCV 1995 pp. 24-
doi:10.1109/ICCV.1995.466929 /iccv/1995/rao1995iccv-object/

Abstract

A general-purpose object indexing technique is described that combines the virtues of principal component analysis with the favorable matching properties of high-dimensional spaces to achieve high-precision recognition. An object is represented by a set of high-dimensional iconic feature vectors comprised of the responses of derivatives of Gaussian filters at a range of orientations and scales. Since these filters can be shown to form the eigenvectors of arbitrary images containing both natural and man-made structures, they are well-suited for indexing in disparate domains. The indexing algorithm uses an active vision system in conjunction with a modified form of Kanerva's (1988, 1993) sparse distributed memory which facilitates interpolation between views and provides a convenient platform for learning the association between an object's appearance and its identity. The robustness of the indexing method was experimentally confirmed by subjecting the method to a range of viewing conditions and the accuracy was verified using a well-known model database containing a number of complex 3D objects under varying pose.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Text

Rao and Ballard. "Object Indexing Using an Iconic Sparse Distributed Memory." IEEE/CVF International Conference on Computer Vision, 1995. doi:10.1109/ICCV.1995.466929

Markdown

[Rao and Ballard. "Object Indexing Using an Iconic Sparse Distributed Memory." IEEE/CVF International Conference on Computer Vision, 1995.](https://mlanthology.org/iccv/1995/rao1995iccv-object/) doi:10.1109/ICCV.1995.466929

BibTeX

@inproceedings{rao1995iccv-object,
  title     = {{Object Indexing Using an Iconic Sparse Distributed Memory}},
  author    = {Rao, Rajesh P. N. and Ballard, Dana H.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1995},
  pages     = {24-},
  doi       = {10.1109/ICCV.1995.466929},
  url       = {https://mlanthology.org/iccv/1995/rao1995iccv-object/}
}