Shaping up simple cell's receptive field of animal vision by ICA and its application in navigation system
Scientists and experts have explored the mechanism of visual systems for decades for smart image processing and pattern recognition in order to satisfy sophisticated engineering applications. In this paper we apply independent component analyses' (ICA) unsupervised learning to natural images, topography images and other special environment images for demonstrating the simple-cell's process in animal vision. Our results confirm an early biological experiment (Nature 228 (1970) 419) about the growth of simple cells in cat's V1 area. Furthermore, by applying ICA methodology and the simplex algorithm, the unsupervised neural synapse's learning can obtain the receptive fields in visual cortex and can simulate the growth of the visual cortex of young animal in the special environment. These findings imply that an input image can be efficiently represented by ICA bases. An application of image matching in the navigation by ICA is shown that the animal visual system method is indeed better than those classical methods at least more than 5%.
independent component analysis gabor function