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To discover the geometric relationships between the cracks, we use the geometrical view to measure the similarity of the cracks. We use the local geometric features extracted by Visual Components to measure the similarity of the cracks. The feature extraction is organized by our expert: first a linear regression is used to compute the length of the cracks to compute the crack length-to-depth aspect ratio. The cracks are then transformed to a binary matrix and the 4D tensor is computed and a k-means clustering algorithm is used to classify the cracks into different groups. The clusters are then used to compute the ratio of the closest cracks to the representative crack and the distance is used to calculate the similarity scores.
We use the geometry views to extract 3D features of the geometry (Sect. 4.4.1). In HOOPS Exchange, the logic behind the extraction is straightforward and we use a flowchart to illustrate it. The world view is a convenient way to understand the complexity of the data and extract the necessary features. We use the crack filtering and dimensionality reduction techniques to reduce the complexity and quickly explore the feature space. We use the crack filtering technique on the attribute views to identify the crack features. We use the dimensionality reduction techniques to reduce the number of cracks to a manageable number.
In order to remove the burden of the decision making, we used the feature ranking method offered by Visual Components . The method ranks features and the order of computation by using a machine learning algorithm to rank the feature importance based on the contribution of the features to the predictions in a multi-output model. The feature importance is then used to project the attributes onto the attribute view.
The geometry extraction of HOOPS Exchange is based on a graph-based approach, which results in a high-quality solution for 3D models that have a large number of holes. It is much simpler to handle complex shapes with holes than other approaches, which suffer from slower processing times.
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