This model will be applied to feature recognition systems in other manufacturing industries.
Automatic feature recognition aids downstream processes such as engineering analysis and manufacturing planning.
Most feature recognition methods use either a cluster-based decomposition or feature line extraction through solid angles or curvature values, followed by graph-based heuristics.
Based on this, two new EEG feature selection approaches, bootstrapped geometric difference (BGD) and network analysis were proposed and applied to feature recognition and classification system.
By applying this to natural gas pipelines, a large amount of location data was collected for data feature recognition and model analysis.
Our earlier work investigated applying such technology to feature recognition, using a testbed interfacing a database system (SQLite) to a CAD modeler (CADfix).
For more complex films however, feature recognition and prioritisation had primary importance.
Our approach to feature recognition and quantification of the drug-response spectrograms is based on projections of the spectrograms onto feature masks.
This research tackles the problem of free-from features recognition.
STL model preprocessing, Region segmentation and automated Feature recognition.
A vital aspect of missing feature recognition is still the estimation of the feature uncertainty.
