A feature recognition processor first translates the design feature model of a part into an intermediate manufacturing feature tree by handling design features according to their properties and the interacting relationships between features.
First, the CAD mesh model is segmented by an improved watershed segmentation algorithm, constructing the region-level representation required by feature recognition.
In this methodology, machining features of the structural parts and their associated attributes are identified by feature recognition technique from the 3 dimensional (3D) part models defined based on model-based definition (MBD) technique.
The selection procedure consists of three major steps: SOM transformation, SVD decomposition followed by feature recognition, and gene selection based on false discovery rate (FDR) estimation (see Methods S1).
At first, results are given for the estimated uncertainty values and RASTA-PLP features in Table 3 and for MFCC features in Table 4. Especially for RASTA-PLP features, results are improved notably by masking and missing feature recognition by modified imputation, averaging an absolute improvement of more than 10% over all tested masks and experiments.
This paper proposes a general, robust approach for mesh denoising by using a combination of bilateral filtering, feature recognition, anisotropic neighborhood searching, and surface fitting and projection techniques.
In the framework, features are captured transparently and hierarchically within a formal OWL ontology, and the feature recognition is achieved by applying an efficient backward-chained ontology reasoner to reason through the ontology.
Its main features are 10 degrees of freedom, light-weight design, and flexible object manipulation by integrating motion control and vision feature recognition.
Although I focused on distinguishing between identical twins, the "identification by unique-feature recognition" can be quickly deployed for individualising an agent in a variety of social contexts (if at least one its unique trait is known by the tracker).
In order to facilitate volumetric feature extraction and feature semantics classification, we pre-process the input B-Rep models by suppressing fillets and rounds before the feature recognition.
