Abstract: Systems, methods, and computer-readable media are disclosed for determining feature representations of 2.5D image data using deep learning techniques. The 2.5D image data may be synthetic image data generated from 3D simulated model data such as 3D CAD data. The 2.5D image data may be indicative of any number of pose estimations/camera poses representing virtual or actual viewing perspectives of an object modeled by the 3D CAD data. A neural network such as a convolution neural network (CNN) may be trained using the 2.5D image data as training data to obtain corresponding feature representations. The pose estimations/camera poses may be stored in a data repository in association with the corresponding feature representations. The learnt CNN may then be used to determine an input feature representation from an input 2.5D image and index the input feature representation against the data repository to determine matching pose estimation(s).

Abstract: Electro-optic assemblies and related materials (e.g., adhesive) tier use therein are generally provided. The adhesive layer may comprise an end-capped polyurethane. Some adhesive layers comprise two or more reactive functional groups (e.g., reactive functional groups configured to react with one or more curing species such that, for example, at least one of the two or more functional groups forms a crosslink). The adhesive may also comprise a chain-extending reagent that includes one or more reactive functional groups. In some embodiments, the adhesive is cured by reacting one or more reactive functional groups with one or more curing species. Curing the adhesive may comprise two or more curing steps. In some embodiments the adhesive layer may comprise one or more cross-linkers.

Abstract: Systems, methods, and computer-readable media are disclosed for automated identification of parts of a parts assembly using image data of the parts assembly and 3D simulated model data of the parts assembly. The 3D simulated model data may be 3D CAD data of the parts assembly. An image of the parts assembly is captured by a mobile device and sent to a back-end server for processing. The back-end server determines a feature representation corresponding to the image and searches a repository to locate a matching feature representation stored in association with a corresponding pose estimation. The matching pose estimation is rendered as an overlay on the image of the parts assembly, thereby enabling automated identification of parts within the image or some user-selected portion of the image.

Abstract: A method for identifying a feature in a first image comprises establishing an initial database of image triplets, and in a pose estimation processor, training a deep learning neural network using the initial database of image triplets, calculating a pose for the first image using the deep learning neural network, comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network, creating a new set of training data including a plurality of error cases identified in a plurality of input images and retraining the deep learning neural network using the new set of training data. The deep learning neural network may be iteratively retrained with a series of new training data sets. Statistical analysis is performed on a plurality of error cases to select a subset of the error cases included in the new set of training data.

Abstract: Method and system for detecting defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect area of potential defect at location on surface of object based on predictable pattern consisting of bright and shadow regions. Kernels are defined corresponding to configurations of dark field illumination sources to enhance detecting potential defect. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypothesis of potential defect at location on surface of object. The hypothesis is classified with a classifier to determine whether the potential defect is true defect. The classifier is trained with training data having characteristics of true defect. The method provides efficient automated detection of micro defects on surface of object.

Abstract: Method and system for detecting defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect area of potential defect at location on surface of object based on predictable pattern consisting of bright and shadow regions. Kernels are defined corresponding to configurations of dark field illumination sources to enhance detecting potential defect. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypothesis of potential defect at location on surface of object. The hypothesis is classified with a classifier to determine whether the potential defect is true defect. The classifier is trained with training data having characteristics of true defect. The method provides efficient automated detection of micro defects on surface of object.

Abstract: Method and system for detecting line defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect areas of potential defects based on predictable pattern consisting of bright and shadow regions. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypotheses of potential defects at locations on surface of object. The hypotheses are classified to determine whether the potential defects are true defects at the locations. Line defect is detected by refining line segments detected on the processed image based on criteria. The criteria include distance from the true defects to the line segments and slops between the true defects and the line segments are less than threshold values.

Abstract: Quinacridone pigments that are surface-functionalized with glycidyl methacrylate, maleic anhydride, or 4-methacryloxyethyl trimellitic anhydride to create a functionalized pigment. The functional groups are then activated to bond hydrophobic polymers, thereby coating the pigment with the hydrophobic polymers. The quinacridone pigments can be used for a variety of applications. They are well-suited for use in electro-optic materials, such as electrophoretic media for use in electrophoretic displays.

Abstract: Method and system for detecting line defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect areas of potential defects based on predictable pattern consisting of bright and shadow regions. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypotheses of potential defects at locations on surface of object. The hypotheses are classified to determine whether the potential defects are true defects at the locations. Line defect is detected by refining line segments detected on the processed image based on criteria. The criteria include distance from the true defects to the line segments and slops between the true defects and the line segments are less than threshold values.

Abstract: Electro-optic assemblies and related materials (e.g., adhesive) for use therein are generally provided. The adhesive layer may comprise an end-capped polyurethane. Some adhesive layers comprise two or more reactive functional groups (e.g., reactive functional groups configured to react with one or more curing species such that, for example, at least one of the two or more functional groups forms a crosslink). The adhesive may also comprise a chain-extending reagent that includes one or more reactive functional groups. In some embodiments, the adhesive is cured by reacting one or more reactive functional groups with one or more curing species. Curing the adhesive may comprise two or more curing steps. In some embodiments the adhesive layer may comprise one or more cross-linkers.

Abstract: Aspects include receiving a request to perform an image classification task in a target domain. The image classification task includes identifying a feature in images in the target domain. Classification information related to the feature is transferred from a source domain to the target domain. The transferring includes receiving a plurality of pairs of task-irrelevant images that each includes a task-irrelevant image in the source domain and in the target domain. The task-irrelevant image in the source domain has a fixed correspondence to the task-irrelevant image in the target domain. A target neural network is trained to perform the image classification task in the target domain. The training is based on the plurality of pairs of task-irrelevant images. The image classification task is performed in the target domain and includes applying the target neural network to an image in the target domain and outputting an identified feature.

Abstract: Embodiments of the present invention provide a computer-implemented method for training an RGB-D classifier for a scene classification task. The method receives task-relevant labeled depth data, task-irrelevant RGB-D data, and a given trained representation in RGB. The method simulates an RGB representation using only the task-irrelevant RGB-D data. The method builds a joint neural network using only the task-irrelevant RGB-D data and the task-relevant labeled depth data.

Abstract: A system and method is disclosed for development of a control application for a controller of an automation system. The controller receives sensor signals associated with perception of a first real component during an execution of the control application program. Activity of a virtual component, including interaction with the real first component, is simulated, the virtual component being a digital twin of a second real component designed for the work environment and absent in the work environment. Virtual data is produced in response to the simulated activity of the virtual component. A control application module determines parameters for development of the control application program using the sensor signals for the first real component and the virtual data. An AR display signal for the work environment is rendered and displayed based on a digital representation of the virtual data during an execution of the control application program.

Abstract: A method for inspecting an object to assist in determining whether the object has a surface defect. The method includes moving the object in a first direction and illuminating the object under ambient lighting conditions. The method also includes capturing at least one image of the object under the ambient lighting conditions while the object moves in the first direction. In addition, the object is illuminated under object lighting conditions and at least one image of the object under the object lighting conditions is captured while the object moves in the first direction to provide at least one object image. Further, the method includes selecting at least one object image having at least one indication of a possible defect to provide images having defect candidates and comparing the defect candidates with previously defined characteristics associated with the defect to facilitate determination of whether a defect exists.

Abstract: For human body representation, bone length or other size characteristic that varies within the population is incorporated into the geometric model of the skeleton. The geometric model may be normalized for shape or tissue modeling, allowing modeling of the shape without dedicating aspects of the data-driven shape model to the length or other size characteristic. Given the same number or extent of components of the data-driven shape model, greater or finer details of the shape may be modeled since components are not committed to the size characteristic.

Abstract: A method for verifying a lighting setup used for inspecting a micro defect. The method includes simulating a scene including a micro defect, light source and imaging device. A position of the light source and imaging device is then optimized to form an optimized simulated setup for viewing micro defect. A shadow calibration reference (SCR) having a simulated shadow field is then rendered in a location. Next, a physical imaging device and light source are positioned based on information from the optimized simulated setup to form an optimized physical setup. A physical SCR based on information from the SCR rendering is fabricated. Next, an image is captured of a physical SCR in a corresponding location associated with each SCR rendering. The optimized physical setup is verified if at least one shadow parameter from the SCR rendering is substantially similar to a corresponding shadow parameter in a corresponding image.

Abstract: For human body representation, bone length or other size characteristic that varies within the population is incorporated into the geometric model of the skeleton. The geometric model may be normalized for shape or tissue modeling, allowing modeling of the shape without dedicating aspects of the data-driven shape model to the length or other size characteristic. Given the same number or extent of components of the data-driven shape model, greater or finer details of the shape may be modeled since components are not committed to the size characteristic.

Abstract: Quinacridone pigments that are surface-functionalized with glycidyl methacrylate, maleic anhydride, or 4-methacryloxyethyl trimellitic anhydride to create a functionalized pigment. The functional groups are then activated to bond hydrophobic polymers, thereby coating the pigment with the hydrophobic polymers. The quinacridone pigments can be used for a variety of applications. They are well-suited for use in electro-optic materials, such as electrophoretic media for use in electrophoretic displays.

Abstract: A method for inspecting an object to assist in determining whether the object has a surface defect. The method includes moving the object in a first direction and illuminating the object under ambient lighting conditions. The method also includes capturing at least one image of the object under the ambient lighting conditions while the object moves in the first direction. In addition, the object is illuminated under object lighting conditions and at least one image of the object under the object lighting conditions is captured while the object moves in the first direction to provide at least one object image. Further, the method includes selecting at least one object image having at least one indication of a possible defect to provide images having defect candidates and comparing the defect candidates with previously defined characteristics associated with the defect to facilitate determination of whether a defect exists.

Abstract: Quinacridone pigments that are surface-functionalized with glycidyl methacrylate, maleic anhydride, or 4-methacryloxyethyl trimellitic anhydride to create a functionalized pigment. The functional groups are then activated to bond hydrophobic polymers, thereby coating the pigment with the hydrophobic polymers. The quinacridone pigments can be used for a variety of applications. They are well-suited for use in electro-optic materials, such as electrophoretic media for use in electrophoretic displays.