A LEARNING VECTOR QUANTIZATION FOR RECOGNITION OF INVARIANT SPATIAL DETECTORS OF PERCEPTUAL PATTERNS CONSTITUENTS
Hanan Hassan, Ali Adian, Bachok M. Taib · 2004
This paper investigates the use of a learning vector quantization (LVQ) neural network trained by invariants and spatial detectors for pattern recognition. Satellite imagery patterns are sensitive to translation, rotation, and scale variability. This motivates the construction of such detectors to constitute perceptual instances for an LVQ network. Influence of certain factors such as the hidden layer neurons, and the learning parameter are investigated.