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We propose to use Sobel- and interpolation-based masks to extract more accurate gradient information in the color difference domain.
GLTP also makes use of the inaccurate Sobel operator for computing the gradient magnitude image when more accurate gradient operators could have been used.
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These include more accurate gradients, convergence acceleration using the Broyden Fletcher Goldfarb Shanno (BFGS) quasi-Newton algorithm as well as faster control derivative calculation algorithms.
Therefore, we should choose a more accurate temperature gradient during the calculation to get a more consistent result with the reality.
Improvements to GLTP such as the use of enhanced pre-processing, a more accurate Scharr gradient operator, dimensionality reduction via PCA and facial component extraction were proposed.
These include the use of a more accurate Scharr gradient operator, dimensionality reduction to reduce the size of the feature vector, and facial component extraction.
In this paper, GLTP is investigated in more detail and further improvements such as the use of enhanced pre-processing, a more accurate Scharr gradient operator, dimensionality reduction via principal component analysis (PCA) and facial component extraction are proposed.
They demonstrated that the new ANFIS is less complex and more accurate than the gradient-based method in ANFIS training.
This should result in a much more accurate representation of the gradient magnitude image.
The multislope approach reveals less expensive and potentially more accurate than the classical gradient techniques.
A steeper gradient implies greater stability due to more accurate hybridisation, whereas a shallower gradient implies less stable binding, whether it be due to the incorporation of 'forced' mismatches due to fast annealing as the temperature decreases away from the Tm, or, due to less annealed dsDNA product formed during slower annealing conditions as the temperature increases above the Tm.
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