Improved texture image classification through the use of a corrosion-inspired cellular automaton

In this paper, the problem of classifying synthetic and natural texture images is addressed. To tackle this problem, an innovative method is proposed that combines concepts from corrosion modeling and cellular automata to generate a texture descriptor. The core processes of metal (pitting) corrosion are identified and applied to texture images by incorporating the basic mechanisms of corrosion in the transition function of the cellular automaton. The surface morphology of the image is analyzed before and during the application of the transition function of the cellular automaton. In each iteration the cumulative mass of corroded product is obtained to construct each of the attributes of the texture descriptor. In a final step, this texture descriptor is used for image classification by applying Linear Discriminant Analysis. The method was tested on the well-known Brodatz and Vistex databases. In addition, in order to verify the robustness of the method, its invariance to noise and rotation were tested. To that end, different variants of the original two databases were obtained through addition of noise to and rotation of the images. The results showed that the method is effective for texture classification according to the high success rates obtained in all cases. This indicates the potential of employing methods inspired on natural phenomena in other fields.Comment: 13 pages, 14 figure

A CA-based model describing fat bloom in chocolate

In this paper a stochastic model based on a cellular automaton (CA) for describing the spatio-temporal dynamics of fat migration in chocolate confectionery, as well as the resulting fat bloom, is conceived. Several hypotheses on the underlying mechanisms for fat migration exist, but there is no consensus on the correct ones. Although many researchers are studying this industrially important phenomenon, few models describing it have been developed. Therefore, the incorporation of different mechanisms of fat migration into a stochastic CA-based model is discussed and the model parameters are investigated for a better understanding of both the model and the complex fat migration phenomenon

Modeling pitting corrosion by means of a 3D discrete stochastic model

Pitting corrosion is difficult to detect, predict and design against. Modeling and simulation can help to increase the knowledge on this phenomenon as well as to make predictions on the initiation and progression of it. A cellular automaton based model describing pitting corrosion is developed based on the main mechanisms behind this phenomenon. Further, a sensitivity analysis is performed in order to get a better insight in the model, after which the information gained from this analysis is employed to estimate the model parameters by means of experimental time series for a metal electrode in contact with different chloride concentrations.FAPESP (11/ 19430-0)CNPqCAPE