Abstract:
Methods for estimating and predicting tooth wear based upon a single 3D digital model of teeth. The 3D digital model is segmented to identify individual teeth within the model. A digital model of a tooth is selected from the segmented model, and its original shape is predicted. The digital model is compared with the predicted original shape to estimate wear areas. A mapping function based upon values relating to tooth wear can also be applied to the selected digital model to predict wear of the tooth.
Abstract:
A non-parametric computer implemented system and method for creating a two dimensional interpretation of a three dimensional biometric representation. The method comprises: obtaining with a camera a three dimensional (3D) representation of a biological feature; determining a region of interest in the 3D representation; selecting an invariant property for the 3D region of interest; identifying a plurality of minutiae in the 3D representation; mapping a nodal mesh to the plurality of minutiae; projecting the nodal mesh of the 3D representation onto a 2D plane; and mapping the plurality of minutiae onto the 2D representation of the nodal mesh. The 2D representation of the plurality of minutiae has a property corresponding to the invariant property in the 3D representation; and the value of the corresponding property in the 2D projection matches the invariant property in the 3D representation.
Abstract:
Three-dimensional test objects provide for assessment of a 3D scanner over a range of scales, frequencies, and/or depths. The test objects may include a substrate having a substantially planar top surface and a plurality of surface features. In some examples, the surface features include a plurality of wedges projecting above the plane of the top surface and extending radially outward from an origin to form a three dimensional star pattern. The shape of the surface features may be periodic or non-periodic. In other examples, the depth of the surface features is decoupled from their lateral frequency.
Abstract:
Systems and methods for authenticating material samples are provided. Characteristic features are measured for a batch of material samples that comprise substantially the same composition and are produced by substantially the same process. The measured characteristic features have respective variability that is analyzed to extract statistical parameters. In some cases, reference ranges are determined based on the extracted statistical parameters for the batch of material samples. The corresponding statistical parameters of a test material sample are compared to the reference ranges to verify whether the test material sample is authentic.
Abstract:
A non-contact friction ridge capture device is described. The device comprises a device housing, the device housing including an electronics compartment and an illumination shield, with an opening between the electronics compartment and the illumination shield into which a user can insert the user's hand. The device further comprising a camera disposed in the electronics compartment for capturing an image of at least one friction ridge surface on a user's hand. The device further comprises a light source disposed in the electronics compartment, the light source emitting light in the direction of the illumination shield, wherein the peak wavelength of emitted light is in the range of 440 to 570 nanometers (nm). The user's hand is not required to contact the device when the camera captures the image of at least one friction ridge surface on a user's hand.
Abstract:
Systems and methods for generating random bits by using physical variations present in material samples are provided. Initial random bit streams are derived from measured material properties for the material samples. In some cases, secondary random bit streams are generated by applying a randomness extraction algorithm to the derived initial random bit streams.
Abstract:
A method for detecting tooth wear using digital 3D models of teeth taken at different times. The digital 3D models of teeth are segmented to identify individual teeth within the digital 3D model. The segmentation includes performing a first segmentation method that over segments at least some of the teeth within the model and a second segmentation method that classifies points within the model as being either on an interior of a tooth or on a boundary between teeth. The results of the first and second segmentation methods are combined to generate segmented digital 3D models. The segmented digital 3D models of teeth are compared to detect tooth wear by determining differences between the segmented models, where the differences relate to the same tooth to detect wear on the tooth over time.
Abstract:
Methods for estimating and predicting tooth wear based upon a single 3D digital model of teeth. The 3D digital model is segmented to identify individual teeth within the model. A digital model of a tooth is selected from the segmented model, and its original shape is predicted. The digital model is compared with the predicted original shape to estimate wear areas. A mapping function based upon values relating to tooth wear can also be applied to the selected digital model to predict wear of the tooth.
Abstract:
Three-dimensional test objects provide for assessment of a 3D scanner over a range of scales, frequencies, and/or depths. The test objects may include a substrate having a substantially planar top surface and a plurality of surface features. In some examples, the surface features include a plurality of wedges projecting above the plane of the top surface and extending radially outward from an origin to form a three dimensional star pattern. The shape of the surface features may be periodic or non-periodic. In other examples, the depth of the surface features is decoupled from their lateral frequency.
Abstract:
Systems and methods for generating random bits by using physical variations present in material samples are provided. Initial random bit streams are derived from measured material properties for the material samples. In some cases, secondary random bit streams are generated by applying a randomness extraction algorithm to the derived initial random bit streams.