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By Raul A. Herrera-Acuña, Vasileios Argyriou, Sergio A. Velastin (auth.), Halimah Badioze Zaman, Peter Robinson, Patrick Olivier, Timothy K. Shih, Sergio Velastin (eds.)

This publication constitutes the refereed lawsuits of the 3rd overseas convention on Advances in visible Informatics, IVIC 2013, held in Selangor, Malaysia, in November 2013. The 4 keynotes and sixty nine papers awarded have been conscientiously reviewed and chosen from a number of submissions. The papers specialise in 4 tracks: machine visions and engineering; special effects and simulation; digital and augmented truth; and visualization and social computing.

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These 20 E. F. Smeaton ratings were therefore excluded from the mean and standard deviation figures generated in Table 1. We also generated figures for the number of moves taken to get to an item worth rating, average rating, and error of predicted rating given by the algorithm. Table 1. Interaction Analysis Data Moves-to-rate Rating Prediction Error List place Mean Std. Dev. 3 Our findings, presented in Table 1, show a number of things. If the algorithm recommended an item that the user rated, it was in 78th place on the list on average, with a large deviation.

Velastin 9. : Modeling multidimensional databases. In: Proceedings of 13th. Int. Conf. on Data Engineering (ICDE), pp. 232–243 (1997) 10. : Modeling multidimensional databases, cubes and cube operations. In: Proceedings of the 10th International Conference on Scientific and Statistical Database Management, pp. 53–62 (1998) 11. : Graphical interfaces for development exploiting the third dimension using Kinect. In: Proceedings of the 9th International Conference on Intelligent Environments, vol. 17, pp.

The more pronounced contrasts between different regions and smoother pixels within a region were also observed in the results of our algorithm which contributed to the overall quality of the reconstruction. When the unmixing performance was quantified using SNR, in general our algorithm could confidently outperform other algorithms since only in the Lena image, the algorithm failed to perform the best. The good performance of the proposed algorithm in the BIS problem can be thought as a result of enforcing orthogonality in the coefficient matrix C which in the same time also enforcing independency in the basis matrix B so that each column of B tends to contain more information of individual image from the mixtures.

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