Neural network based automated texture classification system
H.C.S. Rughooputh, Soonil D.D.V. Rughooputh, Jason M. Kinser · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Texture analysis is an important generic research area of machine vision for detection of patterns in two-dimensional data representations. Despite the wide array of potential areas of application for texture analysis, only a limited number of successful exploitations of texture exist so far since most reported techniques lack the computational tractability required in industry. Neutral network based classifiers have also been proposed for texture recognition. Recent studies of the visual cortex of the cat highlight the role of temporal processing using synchronous oscillations for object identification. In this paper, the original Eckhorn's neural model is modified according to Johnson for texture classification and analysis. A two-dimensional texture image can be mapped into a one-dimensional output function time signature. Each time signature in the form of 8-bit gray level images are further presented to a second PCNN to produce binary barcodes. There is a one-to-one correspondence between these barcoded PCNN outputs and the corresponding input images. The effectiveness of this novel method is demonstrated using 50 textures taken from Brodatz texture album. An n-tuple (RAM-based) neural network is finally used for recognition. Our test results demonstrate that the approach is fast and robust making it suitable for real-time applications.