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First, we employ an illumination estimating algorithm based on morphological closing to decompose an observed image into a reflectance image and an illumination image.
By providing a 3-step interaction estimating algorithm, the closed-loop model-based networked distributed control system (MB-NDCS) is formulated as a time-dependent impulsive system.
Fig. 7 Ginseng weight estimating algorithm procedure: a original loaded image, b conversion to gray image, c image banalization, d block filtering.
In particular, we will develop the efficient estimating algorithm for (e_{m+1}), which fits the coefficients up to 7th order term in the expansion of the global error (E_{m+1}=phi(t_{m+1} -phi_{m+1}) about_{m+1} -phi_{m+1}ze h.
By allowing for over-dispersion parameter to be estimated, we adopted the estimating algorithm of pseudo-likelihood function of Wolfinger/O'Connell 1993 [ 24].
Convergence of the estimating algorithm is formulated by using Lyapunov stability theorems.
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This algorithm has multiple advantages over conventional parameter estimating algorithms.
The parameters were estimanted in Kuznetsov et al 1991 basically by means of mininmal least squares estimate algorithm.
A new approach has been proposed to estimate algorithm parameters automatically.
To estimate algorithm objectivity, we performed the segmentation including centerline positioning twice.
To estimate algorithm performance on novel samples, we performed leave-one-out cross-validation (LOOCV), in which the top-scoring pair as determined by N-1 samples is used to predict the left-out sample class.
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