By Kurt W. Beyer
The profession of computing device visionary Grace Murray Hopper paralleled the meteoric trajectory of the postwar laptop undefined. This BIT describes the parable of “amazing Grace” and tells how she grew to become “the 3rd programmer of the world’s first computer.”
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Additional resources for The Myth of Grace: A BIT of Grace Hopper and the Invention of the Information Age
If any other patch near ‘p’ is considered first for restoration, edges(structure) will not be extended correctly. Hence the pixels on the boundary and the pixels near the edges have to be given a higher priority for restoration process. These priorities are termed as Confidence and Data terms respectively. The patch priority is computed as the product of confidence term and data term. The patch with maximum priority value is restored first. Once when the highest priority patch is filled, there will be a change in the confidence values and the boundary as shown in fig.
53–65. 693652 8. : Discrete feature weighting selection algorithm. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 636–641 (2003) 9. : An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation 188(2), 1567–1579 (2007) 10. : Wairs improving classification accuracy by weighting attributes in the airs classifier. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3759– 3765 (2007) 11. : Medical data mining using BGA and RGA for weighting of features in fuzzy k-NN classification.
P. Soman, and R. Aarthi The restoration process is given by the following algorithm: 1. Extract the manually selected target region and its initial front (boundary). 2. Repeat until there is no boundary pixel: a. Identify the δΩ. If the target region exit. b. Compute priorities P(p) P(p) = C(p) * D(p), where C(p) is the confidence term and D(p) is the data term. c. , p = arg max p for all P(p). d. Find the best matching patch Ψq Є Φ in source region that minimizes d(Ψ p; Ψ q), where d is the Sum of Squared Distance(SSD).
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