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Machines can be pre-programmed to operate automatically with little intervention of the operator.
Under microcomputer control, the analytic procedure could be accomplished automatically without any intervention of the operator.
However, the extent to which the intervention of the operator can affect the final reliability of the pose estimation has not been clarified yet.
The segmentation is the longest process in our data analysis as it requires a manual intervention of the operator and takes about fifteen minutes per disc.
Thus, all of the aforementioned methods suffer from one or more of the following disadvantages: they segment only the most prominent layers, do not exhibit robustness in noisy and varied cases and/or require manual intervention of the operator.
All of the aforementioned methods require relatively high contrast and rely on various initializations, sequential tracking steps and/or require manual intervention of the operator which is extremely time costly and limited in the case of 3D data by human 2D vision (true 3D data can only be perceived as a 2D projection).
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However, it has been observed that this performance is preserved to a greater or lesser extent by the adaptive and palliative interventions of the operators.
This classification procedure combined a k-means clustering of the image pixels and the intervention of an operator, the first author of this paper, who was guided by field knowledge, expertise in photo-interpretation and collaboration with entomologists.
The process is automated, requiring little intervention on behalf of the operator.
If they are found malicious, a decision is made, either automatically, if the accuracy of classification exceeds a desired threshold, or after an intervention of the system's operator for the rejection and non-installation of the application.
Such software is semi-automated as it requires the intervention of an operator to place an ROI and optimize the entire process; however, the mathematical analysis is performed automatically.
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