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First, the CAD mesh model is segmented by an improved watershed segmentation algorithm, constructing the region-level representation required by feature recognition.
The CAD model is segmented into several relevant sub-parts.
The 3D geometrical model is segmented in different sections and the depth profiles are correlated with the different temperature values along the corresponding sections.
The raw particles are composed of two rigid parts flexible to each other: V 1 and V 2. Firstly, the whole 3D volume of initial model is segmented into V 1 and V 2. Then V 2 is subtracted from raw particle images or micrographs, from which the V 1 particle images are re-windowed and subjected to 3D reconstruction, resulting in a refined V 1.
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Both models were segmented in the following tissue types: scalp, skull, CSF, gray (GM) and white (WM) matters.
After the hard-tissue surface models were segmented with the SimPlant®Ortho software, digital markers with an Ø of 2.4 mm were imported as STL files and placed in the landmark configuration position, which serves as reference for the OPA.
The MPEG-7 Content Description artifacts that are captured in our model are Segment, subSegment, and Descriptor.
The model image is segmented at and around landmarks while the test image is oversegmented, e.g., using watershed algorithm, hence it contains a much larger number of segments as compared to the training (model) images.
In our model, the flow is segmented into layers and the pertinent models derived.
However, the 3D model can be segmented from other types of images such as CT images.
The E-beam specimens were created using a 3D CAD model which was segmented into layers of 0.1 mm in order to generate layer information.
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