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Are the methods robust and well controlled?
It was observed that with the proposed features and selection methods, robust and higher classification performance was achieved with fewer numbers of spectral bands enabling the design of simpler machine vision systems.
Similar(58)
However, in order to make the method robust and applicable for commercial field use, the specialised testing and processing device is undergoing refinement and improvement.
These studies pointed out the necessity of new visual feature extraction methods robust to illumination and pose changes.
First, the present findings are supported by three meta-analytic approaches – graphical model fitting methods, robust count methods, and the more traditional meta-analysis based on dprobit effect size indexes.
The methods are robust and the results obtained are within reasonable agreement with observations.
Energy methods are robust and well suited for developing design standards.
Stochastic methods are robust and show a powerful global optimizing ability.
In the experiments, we find that the parameters of our methods are robust and have wide range to choose.
By performing a theoretical analysis, they conclude that none of these methods is robust and applicable for all curve types.
These methods are robust and efficient and are capable of computing interface singularities such as merging and reconnection.
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