Method and apparatus for object recognition
US6038337A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Mar 29, 1996 |
| Grant date | Mar 14, 2000 |
| Priority date | — |
| Expiry date | Mar 29, 2016 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/454
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A hybrid neural network system for object recognition exhibiting local image sampling, a self-organizing map neural network, and a hybrid convolutional neural network. The self-organizing map provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the hybrid convolutional neural network provides for partial invariance to translation, rotation, scale, and deformation. The hybrid convolutional network extracts successively larger features in a hierarchical set of layers. Alternative embodiments using the Karhunen-Loeve transform in place of the self-organizing map, and a multi-layer perceptron in place of the convolutional network are described.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.