An example wear detection system receives a plurality of images from a plurality of sensors associated with a work machine. Individual sensors of the plurality of sensors have respective fields-of-view different from other sensors of the plurality of sensors. The wear detection system identifies a first region of interest and second region of interest associated with the at least one GET. The wear detection system determines a first set of image points and a second set of images points for the at least one GET based on geometric parameters associated with the GET. The wear detection system determines a wear level or loss for the at least one GET based on the GET measurement.

An example wear detection system receives a plurality of images from a plurality of sensors associated with a work machine. Individual sensors of the plurality of sensors have respective fields-of-view different from other sensors of the plurality of sensors. The wear detection system identifies a first region of interest and second region of interest associated with the at least one GET. The wear detection system determines a first set of image points and a second set of images points for the at least one GET based on geometric parameters associated with the GET. The wear detection system determines a wear level or loss for the at least one GET based on the GET measurement.

A method and system for mapping boundary detecting features of at least one source triangulated mesh of known topology to a target triangulated mesh of arbitrary topology. A region of interest in a volumetric image associated with each triangle of the target triangulated mesh is provided to a feature mapping network. The feature mapping network assigns a feature selection vector to each triangle of the target triangulated mesh. The associated region of interest and assigned feature selection vector for each triangle of the target triangulated mesh are provided to a boundary detection network. A predicted boundary based on features of the associated region of interest selected by the assigned feature selection vector is obtained from the boundary detection network.

A method and system for mapping boundary detecting features of at least one source triangulated mesh of known topology to a target triangulated mesh of arbitrary topology. A region of interest in a volumetric image associated with each triangle of the target triangulated mesh is provided to a feature mapping network. The feature mapping network assigns a feature selection vector to each triangle of the target triangulated mesh. The associated region of interest and assigned feature selection vector for each triangle of the target triangulated mesh are provided to a boundary detection network. A predicted boundary based on features of the associated region of interest selected by the assigned feature selection vector is obtained from the boundary detection network.

According to an embodiment, a method for generating a digital data set for fabricating a physical dental bleaching tray useable to deliver an bleaching agent is disclosed. The method includes obtaining a three-dimensional digital representation of a patient’s dentition including teeth and gingiva; segmenting two or more teeth into individual tooth; identifying a facial surface of at least one of the segmented tooth; defining a facial surface portion including a surface area that is at least partly bound by a virtual boundary that is non- interfacing with the gingiva; generating a modified three-dimensional digital representation using the defined facial surface portion; and generating, based on the modified three-dimensional digital representation, the digital data set configured to be used in fabricating the physical dental bleaching tray.

According to an embodiment, a method for generating a digital data set for fabricating a physical dental bleaching tray useable to deliver an bleaching agent is disclosed. The method includes obtaining a three-dimensional digital representation of a patient’s dentition including teeth and gingiva; segmenting two or more teeth into individual tooth; identifying a facial surface of at least one of the segmented tooth; defining a facial surface portion including a surface area that is at least partly bound by a virtual boundary that is non- interfacing with the gingiva; generating a modified three-dimensional digital representation using the defined facial surface portion; and generating, based on the modified three-dimensional digital representation, the digital data set configured to be used in fabricating the physical dental bleaching tray.

A method of detecting defects in a composite layup includes capturing, using an infrared camera, reference images of a reference layup being laid up by a reference layup head. The method also includes manually reviewing the reference images for defects, and generating reference defect masks indicating defects in the reference images. The method further includes training, using the reference images and reference defect masks, a neural network, creating a machine learning model that, given a production image as input, outputs a production defect mask indicating the defect location and the defect type of each defect. The method also includes capturing, using an infrared camera, production images of a production layup being laid up by the production layup head, and applying the model to the production images to automatically generate a production defect masks indicating each defect in the production images.

A method of detecting defects in a composite layup includes capturing, using an infrared camera, reference images of a reference layup being laid up by a reference layup head. The method also includes manually reviewing the reference images for defects, and generating reference defect masks indicating defects in the reference images. The method further includes training, using the reference images and reference defect masks, a neural network, creating a machine learning model that, given a production image as input, outputs a production defect mask indicating the defect location and the defect type of each defect. The method also includes capturing, using an infrared camera, production images of a production layup being laid up by the production layup head, and applying the model to the production images to automatically generate a production defect masks indicating each defect in the production images.

In a driving assistance apparatus, an image acquiring unit acquires a captured image captured by an onboard camera. Based on the captured image acquired by the image acquiring unit, a boundary line recognizing unit recognizes a boundary line that demarcates a traffic lane in which an own vehicle is driving. A road information acquiring unit acquires road information related to a road on which the own vehicle is driving. Based on the road information acquired by the road information acquiring unit, a degree-of-reliability setting unit sets a degree of reliability of the boundary line recognized by the boundary line recognizing unit. Based on the boundary line recognized by the boundary line recognizing unit, a driving assisting unit performs driving assistance of the own vehicle and varies control content of the driving assistance based on the degree of reliability.

In a driving assistance apparatus, an image acquiring unit acquires a captured image captured by an onboard camera. Based on the captured image acquired by the image acquiring unit, a boundary line recognizing unit recognizes a boundary line that demarcates a traffic lane in which an own vehicle is driving. A road information acquiring unit acquires road information related to a road on which the own vehicle is driving. Based on the road information acquired by the road information acquiring unit, a degree-of-reliability setting unit sets a degree of reliability of the boundary line recognized by the boundary line recognizing unit. Based on the boundary line recognized by the boundary line recognizing unit, a driving assisting unit performs driving assistance of the own vehicle and varies control content of the driving assistance based on the degree of reliability.