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Recently, due to the availability of fast and affordable infrared acquisition devices, computer vision beyond the spectrum is also becoming an essential technology for quality control and improvement. For example, during steel strips manufacturing, uneven temperature across the width of the strips during rolling generates defects, due to differences in the contraction of the longitudinal fibers that make up a strip. , 2002). This work proposes a robust method to detect infrared profile patterns in real-time.

8, No. 6, 679-698. ; Lee, S. & Ming J. (1995). Adaptive image segmentation using a genetic algorithm. IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 2, 1543-1567. Bhandarkar, S. M. & Zhang, H. (1999). Image Segmentation Using Evolutionary Computation. IEEE Transactions on Evolutionary Computation, Vol. 3, No. 1, 1-21. Chun, D. N. & Yang H. S. (1996). Robust Image Segmentation Using Genetic Algorithm with a Fuzzy Measure. Pattern Recognition, Vol. 29, No. 7, 1195-1211. Duda, R. ; Hart, P.

Furthermore, this approach takes into account and incorporates important features of HVS as expected and observed increased population sparseness and response decorrelation in comparison to previous Gabor-like and feature extraction models of saliency computation. In progress and future work will deal with other feature dimensions, like colour and motion, in order to allow the model to work with real dynamic scenes; and also with a more depth study on the comparison with human performance. Local Energy Variability as a Generic Measure of Bottom-Up Salience 21 5.

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