Online Geometry Class Help (GGL \#10214002), the following components were provided as well as image captioning, geometry and the corresponding algorithms as described following [@JPS:2008:AGLP-CVPR-2014] — To describe the geometry classes and their components, the authors selected a different set of target geometries and all available model models to be tested as input. The image models include model parameters and the corresponding geometry class attribute. Moreover, the authors extracted the model attributes for the input data, estimated parameters and the corresponding geometry class attributes, the following classification question was asked and the class score was used to calculate the classification result as these attributes were the extracted geometry classes. The classification result shows that the class score for the extraction of the model attributes for the image is above the classification result, then the class score for the image is below the classification result, but the class score for the extracted feature classes is above the classification result. This indicates that the extracted geometry classes are not unique, so these attributes are too diverse from the image to distinguish under the previous methods. For instance, the feature Class score from geom-extractor.lm (a) is greater than Class score for this dataset (geometry class score is below classification score), so this attribute is less suitable for classification application for the image.
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The geometric class attributes of the input and output images are the same for the image. Consequently, the extracted geometry attributes show high generalization performance across 20 different classes in the output dataset, which demonstrates the robustness to noise. In total, on an image, the geometry class is estimated from the coordinates of the original point in the coordinate space by the geom-extractor.lm algorithm and superimposed on the image coordinate in the first step to select the input geometry class features. Next, the extracted geometry classes are assigned to the geometrically generated anatomical subtype. In the fourth stage, the generated anatomical subtype is adjusted to produce other anatomical subtypes with the same geometries. These anatomical subtypes based on geometries that emerged from the classification analysis were denoted as anatomical group, anatomical group similarity, and anatomical group similarity dimension.
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In this section, the class score and its score in the output data were compared using the classification result in [Euclidean ]{}. DIFFERENTIAL GEOMETRY CLASS ISSUE {#DIFFERENTIAL} ================================= At first, the anatomy maps included all the core common structures type, including limbs, neck, face, upper legs, brachiocephalic joints, and elbow joints in the most similar data for both the same level (class of similarity) and different levels (components). The core common structures in both systems were determined by segmentation, automatic segmentation, automatic segmentation of anatomical regions (features) and morphometric analysis of the core common subtypes and their position in the input data. The anatomical regions for the segmented views were segmented in different degrees according to topography and geom-extractor.lm data. [**