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Neural networks for data compression and invariant image recognition
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Gardner, Sheldon |
| Copyright Year | 1989 |
| Description | An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability. |
| File Size | 8396446 |
| File Format | |
| Language | English |
| Publisher Date | 1989-11-01 |
| Access Restriction | Open |
| Subject Keyword | Retinal Images Image Processing Neural Nets Coding Data Compression Chips Waveforms Very Large Scale Integration Spatial Filtering Shapes Mammals Fast Fourier Transformations Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
