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Invariant Pattern Recognition of 2 D Images Using Neural Networksand Frequency-Domain Representation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Castrodecastro, Esar C. De Amaralamaral, Nelson Francopfranco, Paulo Roberto G. |
| Copyright Year | 2007 |
| Abstract | Frequency domain representation of two dimensional gray-level images is used to develop a pattern recognition method that is invariant to rotation, translation and scaling. Frequency domain representation is a natural feature detector that allows the use of only few directions of highest energy as training data for a set of Artiicial Neural Networks (ANNs). We developed a new algorithm that uses the spectral information stored in these ANNs to compare a given image with a known pattern, determining the relative translation between them and yielding a measure of their similarity. The representation and method we adopted has the advantage of leaving only the rotation of the object as a free parameter to be determined by the algorithm. We minimize the spectral resolution noise using Spectral Directional Filtering. Our experimental results indicate that the proposed method has excelent discriminating power. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.ualberta.ca/~amaral/papers/patrec_icnn97.ps.gz |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Artificial neural network Departure - action Detectors Entity Name Part Qualifier - adopted Neural Network Simulation Pattern Recognition Population Parameter Test scaling algorithm |
| Content Type | Text |
| Resource Type | Article |
