Abstract:
Multi-spectral images are becoming more common in industrial inspection tasks where the colour is used as a quality measure. In this paper we propose a spectral cooccurrence matrix-based method to analyse multi-spectral texture images, in which every pixel contains a measured colour spectrum. We first quantise the spectral domain of the multi-spectral images using the Self-Organising Map (SOM). Next we label the spectral domain according to the quantised spectra. In the spatial domain, we represent a multi-spectral texture using the spectral cooccurrence matrix, which we calculate from the labelled image. In the experimental part of this paper, we present the results of segmenting natural multi-spectral textures. We compared the k-nearest neighbour (k-NN) classifier and the multilayer perceptron (MLP) neural network-based segmentation results of the multi-spectral and RGB colour textures.
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Received: 15 September 1998, Received in revised form: 25 January 1999, Accepted: 22 March 1999
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Hauta-Kasari, M., Parkkinen, J., Jaaskelainen, T. et al. Multi-spectral Texture Segmentation Based on the Spectral Cooccurrence Matrix. Pattern Analysis & Applications 2, 275–284 (1999). https://doi.org/10.1007/s100440050036
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DOI: https://doi.org/10.1007/s100440050036